J Forensic Sci, November 2014, Vol. 59, No. 6 doi: 10.1111/1556-4029.12542 Available online at: onlinelibrary.wiley.com
PAPER DIGITAL & MULTIMEDIA SCIENCES Ana Slot,1 M.Sc.; and Zeno J.M.H. Geradts,1 Ph.D.
The Possibilities and Limitations of Forensic Hand Comparison*,†
ABSTRACT: On recordings of certain crimes, the face is not always shown. In such cases, hands can offer a solution, if they are completely
visible. An important aspect of this study was to develop a method for hand comparison. The research method was based on the morphology, anthropometry, and biometry of hands. A new aspect of this study was that a manual and automated test were applied, which, respectively, assess many features and provide identification rates quickly. An important observation was that good quality images can provide sufficient hand details. The most distinctive features were the length/width ratio, the palm line pattern and the quantity of highly distinctive features present, and how they are distributed. The results indicate that experience did not improve the identification rates, while the manual test did. Intra-observer variability did not influence the results, whereas hands of relatives were frequently misjudged. Both tests provided high identification rates.
KEYWORDS: forensic science, biometry, image analysis, anthropometry, biometric identification, hand comparison
As a result of the expanded availability of digital cameras and the increasing applicability of the internet as an instrument for the circulation of indecent pictures, methods of comparison between two sets of images are becoming progressively important. Traditionally, the identification of perpetrators from this type of evidence through physical characteristics has taken place via facial features. As criminals—with particular reference to sex offender cases, pedophile cases, kidnapings, and trophy killings —are increasingly covering their faces, facial recognition is not always a suitable option (1). Hence, the need has arisen to examine methods through which physical features found in other parts of the body than the face can be compared. The common human elements available for assessment are the offender’s hands, although they are not visible by definition and can be covered by wearing gloves. For that, it has become meaningful to establish objective comparison criteria for offender and suspect hand morphologies. In this way, one can determine whether these characteristics are discriminating. If these features are indeed distinctive, they can assist in investigations (2). Almost everyone has pigmented lesions, especially in sunexposed areas like the hands. Freckles, moles, sunspots, and birthmarks vary in appearance and could be a valuable resource for determining individuality. Generally, these markers are quite constant throughout one’s life (3). Because new lesions may Correction added on July 31, 2014, after first online publication: Figure 11 bar graph legend and corresponding author information for Zeno Geradts were changed. 1 Netherlands Forensic Institute, Department of Digital Technology and Biometrics, Laan van Ypenburg 6, 2497 GB, The Hague, The Netherlands. *Presented at the 65th Annual Meeting of the American Academy of Forensic Sciences, February 18-23, 2013, in Washington, DC. † Funding provided by the Ministry of Security and Justice The Netherlands. Received 6 Mar. 2013; and in revised form 6 Aug. 2013; accepted 5 Oct. 2013. © 2014 American Academy of Forensic Sciences
form during one’s life, current data and relatively recent pictures are required (4–6). Scars, injuries (e.g., tattoos), and impressions (e.g., marks), the so called soft biometric markers, are randomly distributed. This distribution can also be used to characterize the marks and to narrow down the possibilities or even to identify individuals (7–11). To assess the location of features, the hand can be segmented into 14 regions using readily discernible anatomical landmarks (Fig. 1). Then, each hand can be assessed for the number of characteristics found in each region. The quantity of lesions present and how they are distributed across the regions can provide information on how the hands can be differentiated from one another (5,6). The aim of the research project, which formed the basis for this article, was to develop a method for hand comparison. Eventually, the comparison should be carried out in an offender/suspect setup, whereby a picture/recording of the perpetrator should be compared with the picture/recording of a suspect. Two other objectives were to determine whether the hand features are variable enough to be used for identification and whether the image quality is good enough to provide sufficient hand details. This paper is an initial study into the feasibility of forensic hand comparisons. As such, limited numbers of hand comparisons were undertaken. Consequently, another limitation of the study was that primarily good quality images were compared. Therefore, bad quality images require further research. Furthermore, the only two proportions assessed in this study were the length/width ratio and digit ratio. It is possible that other ratios exist, which vary even more. Finally, only hands of the Caucasoid race were examined. It is possible that hands of other races provide different results. Materials and Methods A manual test (checklist) was developed to facilitate the comparison of hands. It was inspired by the checklist for forensic facial comparison, which is the standard for facial comparison in 1559
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FIG. 1––Posterior anatomical view (i.e. back of the left hand): the hand is segmented into 14 regions (5,6).
the Netherlands (12). The purpose of the test was to process the information step by step consistent with a fixed schedule. In this way, the systematic analysis of the material is performed as objectively as possible. Furthermore, a hand (vein) database was available that not only offers a lot of study material, but also Matlab algorithms for left and right hands. These algorithms enable automated testing of images and produce identification rates. All hand images analyzed during this study were tested by both methods to discover which method is more suitable for hand comparison. Images belonging to the hand (vein) database and self-made images (made with a flat scanner Epson Perfection V750 Pro with a resolution of 2550 9 3509 pixels) were used. In general, left hands were investigated, as right hands are plumper causing palm deformation with machine contact and have larger intravariations over time (13). Comparing the images took place by presenting them on a display screen. In Adobe Photoshop, the images were optimized by adjusting the brightness and contrast and zooming in on details. Each image was considered separately and only at last side by side to reduce bias. Comparing the various parts of a hand occurred separately as well. In addition, the remainder of the hand was covered. In this manner, each hand component was reviewed independently from other hand parts. The position, number, and pattern of the palm and/or knuckle crease, and the presence and morphology of the pigmented lesions and scars were reviewed. At the end, the overall shape of the hand, the proportions, and the placement of the hand parts were compared as well (12). The number of detected features and the degree of resemblance between the observed characteristics formed the basis for the conclusion of the examination. These aspects can influence the support for one of the two hypotheses, which, respectively, include the assumption that the images concern the same person and that these images do not regard the same person. Checklist Worldwide, there are several techniques currently applied for facial comparison. Within the United Kingdom, for example, holistic comparison, morphological analysis, photoanthropometry, and superimposition are practiced (14). In the Netherlands, there are two main techniques currently applied for facial comparison: holistic comparison and morphological analysis. Selection of the suitable method depends on the image quality, the education and experience of the investigator, and the purpose of the testing (12).
FIG. 2––(Left) Guidelines for measuring the hand length/width ratio (length/width) and (right) digit ratio (2D/4D). In this case the index finger and ring finger are approximately equal, resulting in a 2D:4D ratio of circa 1.
The checklist used in this study is based on the morphology and anthropometry of hands. It contains qualitative features (the majority) and quantitative features (or proportions). Two proportions are assessed, comprising the length/width ratio and digit ratio (Fig. 2). Initial results showed that i.a. the length/width ratio appeared to be one of the most distinctive features tested. Therefore, it was decided to maintain this ratio in the checklist and in further tests. It is known that there is a clear correlation between finger length and quantity of the male sex hormone testosterone that a fetus is exposed to in the womb. In women, the index and ring fingers are commonly comparable in length, when measured from the crease between region 7–11 and 9–12 to the fingertip (Fig. 1). In men, the ring finger is often substantially longer than the index (15). The test also includes hand palm specific features (e.g., the palm line pattern), hand back specific features (e.g., the vein structure pattern and nails), and shared features (e.g., the shape, proportions, skin color, and highly distinctive features) (16,17). Table 1 shows the checklist. Matlab Algorithms The hand (vein) database used in this study belongs to the Bosporus University in Istanbul and is intended for biometric research. It contains hands of many individuals made with a flat scanner within a period of 4 years and Matlab algorithms for left and right hand palms. The hand palm database contains two directories (one with left hands and one with right hands), which are suitable for Matlab. They both comprise 1926 images belonging to 642 individuals, so in each directory, three images per person are available. The hand back/vein database contains five directories (four with left hands under different conditions and one with right hands). They all comprise 300 images belonging to 100 individuals, so in each directory, three images per person are available. The scanning conditions were as follows. Positioning resources were not applied. The subjects laid their hands relaxed on the scanner in any position. The only limitation was that their fingers were hold separately. The subjects did not have to remove their rings and watches. The spurious whitish stains caused by unequal hand pressure applied on the scanner do not influence the accuracy of the results, as these artefacts can easily be removed during one of the processing steps by high-pass filtering. The algorithms need 5–6 sec to investigate hands and can analyze hand shape and texture information (namely, palm print patterns), compare images, and provide identification rates (13,20,21).
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TABLE 1––Checklist for hand comparison: S (similarity), NO (not observable), D (difference) and SI (strongly individualizing). Similarities and Differences Features
Explanation
Characteristics that can be found on palm and back of the hand Shape Proportions Hand length (Fig. 2) (18) Hand width (Fig. 2) (18) Length/width ratio (length/width) Digit ratio (2D/4D) (Fig. 2) (19) Skin colour Characteristic features (score by region below)
Pigmented lesions
Scars and injuries Other typica Characteristics that can be found on palm of the hand Anterior anatomical view (left/right hand) (based on Fig. 1) Fingers Thumb Index finger Middle finger Ring finger Little finger Body of the hand
Palm line pattern Characteristics that can be found on back of the hand Posterior anatomical view (left/right hand) (based on Fig. 1) Fingers Thumb Index finger Middle finger Ring finger Little finger Body of the hand
Remarks
S
NO
D
SI
Explanation for Difference Under Assumption Images Belong to Same Person
Explanation for SI Similarity Under Assumption Images Belong to Different Persons
172 mm (Female) 189 mm (Male) 74 mm (Female) 84 mm (Male) 0.965 (Female) 0.947 (Male) Freckles Moles Sun spots Birthmarks Scars Tattoos Impressions Wrinkles Region Region Region Region Region Region Region Region Region Region Region Region Region Region
1 6 2 7 3 8 4 9 5 10 11 12 13 14
Region Region Region Region Region Region Region Region Region Region Region Region Region Region
1 6 2 7 3 8 4 9 5 10 11 12 13 14
Veins in the back of the hand Hair growth Nails Lunula area Nail plate size Shape
During this study thresholds were applied, as the algorithms produce identifications rates. These rates have to be compared with a critical value in order to be informative. The threshold was set at 0.60, as from this value there are no false positives anymore (Fig. 3). As mentioned before, the hand database contains 1926 images belonging to 642 individuals. In order to tune the algorithms and to determine the threshold, the images
belonging to 55 random persons were selected. The images of the other 587 individuals were used to generate the data for the first subtest. Hence, the images used for tuning and testing were not the same. In other words, the unjust imprisonment of innocent people outweighs the unjust liberation of criminals. So, identification rates less than 0.60 were classified as mismatches, whereas rates greater than or equal to 0.60 were classified as matches.
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FIG. 4––A match: the pair of images that both tests identified wrongly. FIG. 3––Conclusions are based on subjective thresholds for acceptable differences between measurements. Biometric systems run at a low FAR (instead of EER) to deliver high safety. Therefore, the threshold was set at 0.60, as from this value (x ≥ 0.57; y = 0) there are no false positives anymore. So, if the IR was beneath 0.60 the two compared images were grouped as mismatch; if it was above this value they were classified as match.
Results Good Quality Images A blind test included 10 pairs of hand palm images from the hand database (the first subtest), 15 pairs of self-made hand palm images (the second subtest), and 15 pairs of self-made hand back images (the third subtest), which either matched or did not match. All images had a good quality. Hence, images with a high resolution and sharpness were used. The test was created by a colleague to minimize/avoid bias. The images were tested both by manually (with the help of the checklist) and by the algorithms. After the blind test was performed by the checklist, the results were compared with the real answers. Soon, it became evident which features were the most distinctive ones, as their outcomes were (nearly) the same in case of matches and (substantially) differed from one another in case of mismatches. For the other features, this was less/not the case. The results of the blind test showed that the length/width ratio, the palm line pattern and the quantity of highly distinctive features present, and how they are distributed appeared to be the most distinctive features when tested by the checklist. Regarding the algorithms, person identification is founded on the distance of feature vectors, namely the Independent Component Analysis (ICA) characteristics. The makers of the database assessed various feature schemes, and the ICA features seemed to perform superior to all other characteristics judged. The features comprise independent parts of the hand shape; the classifier is based on the Euclidean distance. Feature extraction and recognition assesses the complete scene image, including the normalized hand and its surroundings. The realized performance of 99.65% true identifications for a population of 756 individuals is very promising (20,21). In case of the first subtest, 10 pairs of hand palm images from the hand database were compared. The purpose of this test was to examine whether the manual and automated tests function and which of these tests provides more accurate results. Both methods identified 90% of the images correctly and 10% (a false negative) wrongly. In this study, the term “identified correctly” is used to signify that true matches are declared as such and that true nonmatches are declared as such. In both cases, the false negative concerned the same pair of photographs (Fig. 4).
During the second subtest, 15 pairs of self-made hand palm images were compared. The purpose of this test was to examine whether the manual and automated tests still function when selfmade photographs are used and to check whether within an individual the right hand can be discriminated from the left hand. Suppose there is still no suspect, but there are a few cases with images that probably belong to the same person. However, in one case, an image of a right hand is available, whereas in the other case only a left hand image is visible. In such cases, it can be useful to compare left and right hand from presumably the same individual. In order to process the self-made images by the algorithms, a “Matlab formula” had to be developed. The formula included the following steps: scanning the hand palms/ backs with a black background and saving them as JPEGs, placing them in a 382 9 525 pixels compartment in Adobe Photoshop, and coloring the remaining edges black (Fig. 5). In case of two pairs of images, left and right hand from the same individual were compared. The right hand had been flipped horizontally, so the examiner was not aware of this during the test. The results of the second subtest showed that the algorithms compare a left hand with a right hand less accurately than the manual test. Eighty-seven percent of the images was identified correctly with the help of the checklist; two false positives were observed. The algorithms identified 80% of the images correctly; two false negatives (the two pairs of images whereby left and right hand from the same individual were compared) and one false positive were observed. During the third subtest, 15 pairs of self-made hand back images were compared. The purpose of this test was to examine whether the manual and automated tests still function when selfmade hand back images are used. The results of the third subtest
FIG. 5––The results after (left) scanning the hand palm with a black background and saving it as JPEG and (right) placing it in a 382 9 525 pixels compartment in Adobe Photoshop and colouring the remaining edges black.
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FIG. 6––(Left) Image of a waving politician, (middle) image after following the usual Matlab recipe and (right) image after enlarging the hand in such a way that its size is equal to the size of the hand it has to be compared with.
Bad Quality Images The final goal of forensic hand comparison is the possibility to compare bad quality images, as images from real cases are often of comparable quality. For that, hand images of poor quality were acquired from the internet. Hence, images with a low resolution and sharpness were used. It concerned images of six waving celebrities, such as politicians and members of royal families. Two images of each person were generated. Apart from the poor quality and size, the hand palms are often clearly visible on this type of images, and the usual Matlab formula could be followed (although now the entire background had to be colored black). When comparing images of one person, two different images of the same person were compared. It was assumed that the pictures belonged to one and the same person. When comparing images of two individuals, obviously, two images of different individuals were compared. Figure 6 consists of three subfigures. The left subfigure shows an image of a waving politician. In the middle subfigure, the image is shown after following the usual Matlab formula. However, now false positives were gained. The right subfigure shows the image after enlarging the hand in such a way that its size resembles the size of the hand it has to be compared with. This approach, however, produced false negatives. The test results showed that the size is very important, as the algorithms consider hand shape and hand texture information. Furthermore, the absolute size is considered instead of ratios, and the position of hands is very important. One image was copied and that copy was renamed. The original image and the copy were analyzed twice by the algorithms. The first time the copy had been reduced. Matlab gave as a result that the hands belong to different persons. The second time the copy remained unchanged. Matlab gave as a result that the hands belong to the same person. In case of the reduced hand, only the absolute size had been changed, the ratios remained unchanged. Matlab apparently found a mismatch instead of a match, because it had taken into account the absolute size instead of the ratios. Currently, the algorithms are not robust enough for processing bad quality images. False positives and false negatives were only generated using the algorithms. Due to the presence of bias, the bad quality images were not analyzed by the checklist. However, a few images were displayed on a screen and zoomed in on details. Immediately, some similarities and differences became obvious, for instance concerning the palm line pattern. Therefore, manual
comparison of bad quality images seems more promising, although more research is required. Ratios After the 10 pairs of hand palm images from the hand database and the 15 pairs of self-made hand palm images were combined, a sample of 25 pairs of hand palm images was obtained. Now, the checklist’s identification rate was 88%, and the algorithms’ identification rate was 84%. The length/width ratios and the digit ratios of these 50 hand images were plotted in a chart. When interpreting Fig. 7, two standard deviations can be considered: the standard deviation of the length/width ratio and the digit ratio and their impact on the conclusion. The standard deviation of the length/width ratio is equal to 0.11, whereas the standard deviation of the digit ratio is equal to 0.04. In general, obtaining a small standard deviation is desirable. However, in this case, a large standard deviation is preferable, as the purpose of the test is to determine which ratio is more distinctive and therefore more suitable for identification purposes. The standard deviation of the length/width ratio is larger than the standard deviation of the digit ratio. Therefore, this ratio seems to be more distinctive than the digit ratio (assuming that no measurement inaccuracies have occurred). Descriptive statistics (range, mean and variance) were calculated to compare both ratios to one another. Additional statistical techniques were employed to determine which is the most discriminative feature: the length/width ratio or the digit ratio. The most discriminative feature is the one with the largest variation. A test that compares the variances of the two samples with each other was applied to see whether they differ from one another. Variability of hand palm raƟos 2.5
2.3
2.1
1.9
1.7
Length/width ratio
RaƟo
showed that the algorithms assess hand backs less accurately than the manual test. Hundred percent of the images was identified correctly with the help of the checklist. The algorithms identified 73% of the images correctly; four false negatives were observed.
Digit ratio Average
1.5
Average 1.3
1.1
0.9
0.7
Person number
FIG. 7––The length/width ratios and digit ratios of 50 hand palm images, showing that the length/width ratios seem to vary more than the digit ratios.
JOURNAL OF FORENSIC SCIENCES
In case of the length/width ratio, the Lillifors test for normality showed a test statistic of 0.0881 with a p-value of 0.42. The test demonstrated that the length/width ratio is normally distributed. In case of the digit ratio, the Lillifors test for normality showed a test statistic of 0.1705 with a p-value of
PAPER DIGITAL & MULTIMEDIA SCIENCES Ana Slot,1 M.Sc.; and Zeno J.M.H. Geradts,1 Ph.D.
The Possibilities and Limitations of Forensic Hand Comparison*,†
ABSTRACT: On recordings of certain crimes, the face is not always shown. In such cases, hands can offer a solution, if they are completely
visible. An important aspect of this study was to develop a method for hand comparison. The research method was based on the morphology, anthropometry, and biometry of hands. A new aspect of this study was that a manual and automated test were applied, which, respectively, assess many features and provide identification rates quickly. An important observation was that good quality images can provide sufficient hand details. The most distinctive features were the length/width ratio, the palm line pattern and the quantity of highly distinctive features present, and how they are distributed. The results indicate that experience did not improve the identification rates, while the manual test did. Intra-observer variability did not influence the results, whereas hands of relatives were frequently misjudged. Both tests provided high identification rates.
KEYWORDS: forensic science, biometry, image analysis, anthropometry, biometric identification, hand comparison
As a result of the expanded availability of digital cameras and the increasing applicability of the internet as an instrument for the circulation of indecent pictures, methods of comparison between two sets of images are becoming progressively important. Traditionally, the identification of perpetrators from this type of evidence through physical characteristics has taken place via facial features. As criminals—with particular reference to sex offender cases, pedophile cases, kidnapings, and trophy killings —are increasingly covering their faces, facial recognition is not always a suitable option (1). Hence, the need has arisen to examine methods through which physical features found in other parts of the body than the face can be compared. The common human elements available for assessment are the offender’s hands, although they are not visible by definition and can be covered by wearing gloves. For that, it has become meaningful to establish objective comparison criteria for offender and suspect hand morphologies. In this way, one can determine whether these characteristics are discriminating. If these features are indeed distinctive, they can assist in investigations (2). Almost everyone has pigmented lesions, especially in sunexposed areas like the hands. Freckles, moles, sunspots, and birthmarks vary in appearance and could be a valuable resource for determining individuality. Generally, these markers are quite constant throughout one’s life (3). Because new lesions may Correction added on July 31, 2014, after first online publication: Figure 11 bar graph legend and corresponding author information for Zeno Geradts were changed. 1 Netherlands Forensic Institute, Department of Digital Technology and Biometrics, Laan van Ypenburg 6, 2497 GB, The Hague, The Netherlands. *Presented at the 65th Annual Meeting of the American Academy of Forensic Sciences, February 18-23, 2013, in Washington, DC. † Funding provided by the Ministry of Security and Justice The Netherlands. Received 6 Mar. 2013; and in revised form 6 Aug. 2013; accepted 5 Oct. 2013. © 2014 American Academy of Forensic Sciences
form during one’s life, current data and relatively recent pictures are required (4–6). Scars, injuries (e.g., tattoos), and impressions (e.g., marks), the so called soft biometric markers, are randomly distributed. This distribution can also be used to characterize the marks and to narrow down the possibilities or even to identify individuals (7–11). To assess the location of features, the hand can be segmented into 14 regions using readily discernible anatomical landmarks (Fig. 1). Then, each hand can be assessed for the number of characteristics found in each region. The quantity of lesions present and how they are distributed across the regions can provide information on how the hands can be differentiated from one another (5,6). The aim of the research project, which formed the basis for this article, was to develop a method for hand comparison. Eventually, the comparison should be carried out in an offender/suspect setup, whereby a picture/recording of the perpetrator should be compared with the picture/recording of a suspect. Two other objectives were to determine whether the hand features are variable enough to be used for identification and whether the image quality is good enough to provide sufficient hand details. This paper is an initial study into the feasibility of forensic hand comparisons. As such, limited numbers of hand comparisons were undertaken. Consequently, another limitation of the study was that primarily good quality images were compared. Therefore, bad quality images require further research. Furthermore, the only two proportions assessed in this study were the length/width ratio and digit ratio. It is possible that other ratios exist, which vary even more. Finally, only hands of the Caucasoid race were examined. It is possible that hands of other races provide different results. Materials and Methods A manual test (checklist) was developed to facilitate the comparison of hands. It was inspired by the checklist for forensic facial comparison, which is the standard for facial comparison in 1559
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FIG. 1––Posterior anatomical view (i.e. back of the left hand): the hand is segmented into 14 regions (5,6).
the Netherlands (12). The purpose of the test was to process the information step by step consistent with a fixed schedule. In this way, the systematic analysis of the material is performed as objectively as possible. Furthermore, a hand (vein) database was available that not only offers a lot of study material, but also Matlab algorithms for left and right hands. These algorithms enable automated testing of images and produce identification rates. All hand images analyzed during this study were tested by both methods to discover which method is more suitable for hand comparison. Images belonging to the hand (vein) database and self-made images (made with a flat scanner Epson Perfection V750 Pro with a resolution of 2550 9 3509 pixels) were used. In general, left hands were investigated, as right hands are plumper causing palm deformation with machine contact and have larger intravariations over time (13). Comparing the images took place by presenting them on a display screen. In Adobe Photoshop, the images were optimized by adjusting the brightness and contrast and zooming in on details. Each image was considered separately and only at last side by side to reduce bias. Comparing the various parts of a hand occurred separately as well. In addition, the remainder of the hand was covered. In this manner, each hand component was reviewed independently from other hand parts. The position, number, and pattern of the palm and/or knuckle crease, and the presence and morphology of the pigmented lesions and scars were reviewed. At the end, the overall shape of the hand, the proportions, and the placement of the hand parts were compared as well (12). The number of detected features and the degree of resemblance between the observed characteristics formed the basis for the conclusion of the examination. These aspects can influence the support for one of the two hypotheses, which, respectively, include the assumption that the images concern the same person and that these images do not regard the same person. Checklist Worldwide, there are several techniques currently applied for facial comparison. Within the United Kingdom, for example, holistic comparison, morphological analysis, photoanthropometry, and superimposition are practiced (14). In the Netherlands, there are two main techniques currently applied for facial comparison: holistic comparison and morphological analysis. Selection of the suitable method depends on the image quality, the education and experience of the investigator, and the purpose of the testing (12).
FIG. 2––(Left) Guidelines for measuring the hand length/width ratio (length/width) and (right) digit ratio (2D/4D). In this case the index finger and ring finger are approximately equal, resulting in a 2D:4D ratio of circa 1.
The checklist used in this study is based on the morphology and anthropometry of hands. It contains qualitative features (the majority) and quantitative features (or proportions). Two proportions are assessed, comprising the length/width ratio and digit ratio (Fig. 2). Initial results showed that i.a. the length/width ratio appeared to be one of the most distinctive features tested. Therefore, it was decided to maintain this ratio in the checklist and in further tests. It is known that there is a clear correlation between finger length and quantity of the male sex hormone testosterone that a fetus is exposed to in the womb. In women, the index and ring fingers are commonly comparable in length, when measured from the crease between region 7–11 and 9–12 to the fingertip (Fig. 1). In men, the ring finger is often substantially longer than the index (15). The test also includes hand palm specific features (e.g., the palm line pattern), hand back specific features (e.g., the vein structure pattern and nails), and shared features (e.g., the shape, proportions, skin color, and highly distinctive features) (16,17). Table 1 shows the checklist. Matlab Algorithms The hand (vein) database used in this study belongs to the Bosporus University in Istanbul and is intended for biometric research. It contains hands of many individuals made with a flat scanner within a period of 4 years and Matlab algorithms for left and right hand palms. The hand palm database contains two directories (one with left hands and one with right hands), which are suitable for Matlab. They both comprise 1926 images belonging to 642 individuals, so in each directory, three images per person are available. The hand back/vein database contains five directories (four with left hands under different conditions and one with right hands). They all comprise 300 images belonging to 100 individuals, so in each directory, three images per person are available. The scanning conditions were as follows. Positioning resources were not applied. The subjects laid their hands relaxed on the scanner in any position. The only limitation was that their fingers were hold separately. The subjects did not have to remove their rings and watches. The spurious whitish stains caused by unequal hand pressure applied on the scanner do not influence the accuracy of the results, as these artefacts can easily be removed during one of the processing steps by high-pass filtering. The algorithms need 5–6 sec to investigate hands and can analyze hand shape and texture information (namely, palm print patterns), compare images, and provide identification rates (13,20,21).
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TABLE 1––Checklist for hand comparison: S (similarity), NO (not observable), D (difference) and SI (strongly individualizing). Similarities and Differences Features
Explanation
Characteristics that can be found on palm and back of the hand Shape Proportions Hand length (Fig. 2) (18) Hand width (Fig. 2) (18) Length/width ratio (length/width) Digit ratio (2D/4D) (Fig. 2) (19) Skin colour Characteristic features (score by region below)
Pigmented lesions
Scars and injuries Other typica Characteristics that can be found on palm of the hand Anterior anatomical view (left/right hand) (based on Fig. 1) Fingers Thumb Index finger Middle finger Ring finger Little finger Body of the hand
Palm line pattern Characteristics that can be found on back of the hand Posterior anatomical view (left/right hand) (based on Fig. 1) Fingers Thumb Index finger Middle finger Ring finger Little finger Body of the hand
Remarks
S
NO
D
SI
Explanation for Difference Under Assumption Images Belong to Same Person
Explanation for SI Similarity Under Assumption Images Belong to Different Persons
172 mm (Female) 189 mm (Male) 74 mm (Female) 84 mm (Male) 0.965 (Female) 0.947 (Male) Freckles Moles Sun spots Birthmarks Scars Tattoos Impressions Wrinkles Region Region Region Region Region Region Region Region Region Region Region Region Region Region
1 6 2 7 3 8 4 9 5 10 11 12 13 14
Region Region Region Region Region Region Region Region Region Region Region Region Region Region
1 6 2 7 3 8 4 9 5 10 11 12 13 14
Veins in the back of the hand Hair growth Nails Lunula area Nail plate size Shape
During this study thresholds were applied, as the algorithms produce identifications rates. These rates have to be compared with a critical value in order to be informative. The threshold was set at 0.60, as from this value there are no false positives anymore (Fig. 3). As mentioned before, the hand database contains 1926 images belonging to 642 individuals. In order to tune the algorithms and to determine the threshold, the images
belonging to 55 random persons were selected. The images of the other 587 individuals were used to generate the data for the first subtest. Hence, the images used for tuning and testing were not the same. In other words, the unjust imprisonment of innocent people outweighs the unjust liberation of criminals. So, identification rates less than 0.60 were classified as mismatches, whereas rates greater than or equal to 0.60 were classified as matches.
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JOURNAL OF FORENSIC SCIENCES
FIG. 4––A match: the pair of images that both tests identified wrongly. FIG. 3––Conclusions are based on subjective thresholds for acceptable differences between measurements. Biometric systems run at a low FAR (instead of EER) to deliver high safety. Therefore, the threshold was set at 0.60, as from this value (x ≥ 0.57; y = 0) there are no false positives anymore. So, if the IR was beneath 0.60 the two compared images were grouped as mismatch; if it was above this value they were classified as match.
Results Good Quality Images A blind test included 10 pairs of hand palm images from the hand database (the first subtest), 15 pairs of self-made hand palm images (the second subtest), and 15 pairs of self-made hand back images (the third subtest), which either matched or did not match. All images had a good quality. Hence, images with a high resolution and sharpness were used. The test was created by a colleague to minimize/avoid bias. The images were tested both by manually (with the help of the checklist) and by the algorithms. After the blind test was performed by the checklist, the results were compared with the real answers. Soon, it became evident which features were the most distinctive ones, as their outcomes were (nearly) the same in case of matches and (substantially) differed from one another in case of mismatches. For the other features, this was less/not the case. The results of the blind test showed that the length/width ratio, the palm line pattern and the quantity of highly distinctive features present, and how they are distributed appeared to be the most distinctive features when tested by the checklist. Regarding the algorithms, person identification is founded on the distance of feature vectors, namely the Independent Component Analysis (ICA) characteristics. The makers of the database assessed various feature schemes, and the ICA features seemed to perform superior to all other characteristics judged. The features comprise independent parts of the hand shape; the classifier is based on the Euclidean distance. Feature extraction and recognition assesses the complete scene image, including the normalized hand and its surroundings. The realized performance of 99.65% true identifications for a population of 756 individuals is very promising (20,21). In case of the first subtest, 10 pairs of hand palm images from the hand database were compared. The purpose of this test was to examine whether the manual and automated tests function and which of these tests provides more accurate results. Both methods identified 90% of the images correctly and 10% (a false negative) wrongly. In this study, the term “identified correctly” is used to signify that true matches are declared as such and that true nonmatches are declared as such. In both cases, the false negative concerned the same pair of photographs (Fig. 4).
During the second subtest, 15 pairs of self-made hand palm images were compared. The purpose of this test was to examine whether the manual and automated tests still function when selfmade photographs are used and to check whether within an individual the right hand can be discriminated from the left hand. Suppose there is still no suspect, but there are a few cases with images that probably belong to the same person. However, in one case, an image of a right hand is available, whereas in the other case only a left hand image is visible. In such cases, it can be useful to compare left and right hand from presumably the same individual. In order to process the self-made images by the algorithms, a “Matlab formula” had to be developed. The formula included the following steps: scanning the hand palms/ backs with a black background and saving them as JPEGs, placing them in a 382 9 525 pixels compartment in Adobe Photoshop, and coloring the remaining edges black (Fig. 5). In case of two pairs of images, left and right hand from the same individual were compared. The right hand had been flipped horizontally, so the examiner was not aware of this during the test. The results of the second subtest showed that the algorithms compare a left hand with a right hand less accurately than the manual test. Eighty-seven percent of the images was identified correctly with the help of the checklist; two false positives were observed. The algorithms identified 80% of the images correctly; two false negatives (the two pairs of images whereby left and right hand from the same individual were compared) and one false positive were observed. During the third subtest, 15 pairs of self-made hand back images were compared. The purpose of this test was to examine whether the manual and automated tests still function when selfmade hand back images are used. The results of the third subtest
FIG. 5––The results after (left) scanning the hand palm with a black background and saving it as JPEG and (right) placing it in a 382 9 525 pixels compartment in Adobe Photoshop and colouring the remaining edges black.
SLOT AND GERADTS
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THE POSSIBILITIES AND LIMITATIONS OF FHC
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FIG. 6––(Left) Image of a waving politician, (middle) image after following the usual Matlab recipe and (right) image after enlarging the hand in such a way that its size is equal to the size of the hand it has to be compared with.
Bad Quality Images The final goal of forensic hand comparison is the possibility to compare bad quality images, as images from real cases are often of comparable quality. For that, hand images of poor quality were acquired from the internet. Hence, images with a low resolution and sharpness were used. It concerned images of six waving celebrities, such as politicians and members of royal families. Two images of each person were generated. Apart from the poor quality and size, the hand palms are often clearly visible on this type of images, and the usual Matlab formula could be followed (although now the entire background had to be colored black). When comparing images of one person, two different images of the same person were compared. It was assumed that the pictures belonged to one and the same person. When comparing images of two individuals, obviously, two images of different individuals were compared. Figure 6 consists of three subfigures. The left subfigure shows an image of a waving politician. In the middle subfigure, the image is shown after following the usual Matlab formula. However, now false positives were gained. The right subfigure shows the image after enlarging the hand in such a way that its size resembles the size of the hand it has to be compared with. This approach, however, produced false negatives. The test results showed that the size is very important, as the algorithms consider hand shape and hand texture information. Furthermore, the absolute size is considered instead of ratios, and the position of hands is very important. One image was copied and that copy was renamed. The original image and the copy were analyzed twice by the algorithms. The first time the copy had been reduced. Matlab gave as a result that the hands belong to different persons. The second time the copy remained unchanged. Matlab gave as a result that the hands belong to the same person. In case of the reduced hand, only the absolute size had been changed, the ratios remained unchanged. Matlab apparently found a mismatch instead of a match, because it had taken into account the absolute size instead of the ratios. Currently, the algorithms are not robust enough for processing bad quality images. False positives and false negatives were only generated using the algorithms. Due to the presence of bias, the bad quality images were not analyzed by the checklist. However, a few images were displayed on a screen and zoomed in on details. Immediately, some similarities and differences became obvious, for instance concerning the palm line pattern. Therefore, manual
comparison of bad quality images seems more promising, although more research is required. Ratios After the 10 pairs of hand palm images from the hand database and the 15 pairs of self-made hand palm images were combined, a sample of 25 pairs of hand palm images was obtained. Now, the checklist’s identification rate was 88%, and the algorithms’ identification rate was 84%. The length/width ratios and the digit ratios of these 50 hand images were plotted in a chart. When interpreting Fig. 7, two standard deviations can be considered: the standard deviation of the length/width ratio and the digit ratio and their impact on the conclusion. The standard deviation of the length/width ratio is equal to 0.11, whereas the standard deviation of the digit ratio is equal to 0.04. In general, obtaining a small standard deviation is desirable. However, in this case, a large standard deviation is preferable, as the purpose of the test is to determine which ratio is more distinctive and therefore more suitable for identification purposes. The standard deviation of the length/width ratio is larger than the standard deviation of the digit ratio. Therefore, this ratio seems to be more distinctive than the digit ratio (assuming that no measurement inaccuracies have occurred). Descriptive statistics (range, mean and variance) were calculated to compare both ratios to one another. Additional statistical techniques were employed to determine which is the most discriminative feature: the length/width ratio or the digit ratio. The most discriminative feature is the one with the largest variation. A test that compares the variances of the two samples with each other was applied to see whether they differ from one another. Variability of hand palm raƟos 2.5
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showed that the algorithms assess hand backs less accurately than the manual test. Hundred percent of the images was identified correctly with the help of the checklist. The algorithms identified 73% of the images correctly; four false negatives were observed.
Digit ratio Average
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FIG. 7––The length/width ratios and digit ratios of 50 hand palm images, showing that the length/width ratios seem to vary more than the digit ratios.
JOURNAL OF FORENSIC SCIENCES
In case of the length/width ratio, the Lillifors test for normality showed a test statistic of 0.0881 with a p-value of 0.42. The test demonstrated that the length/width ratio is normally distributed. In case of the digit ratio, the Lillifors test for normality showed a test statistic of 0.1705 with a p-value of
