In the last two decades, the number of terrorist attacks has increased significantly. Driven by psychological, political, religious, or ideological reasons, these cruel actions were commenced by different terrorists from different races religions and countries (Aldahadha, 2018). Those who appear in images and videos occupying a large space in the media stream, covering every single part of their bodies, without making sounds or with distorted voices, leaving nothing to expose their identity, thinking that there “is a place to hide” in such a digital world (Bradbury, 2005). However, we found in a large number of cases that those terrorists appear holding up the so-called victory signs, which are V-shapes of their two fingers; the index, and the middle finger. Since these V-shapes might be the only visible evidence in such situations, we target them for identifying those persons.

Hand shape biometrics is not new technology, it has been around since the 1970s, driven by its low-cost, user-friendly and reliability the number of its publications has increased exponentially (Dutagac, et al., 2008). The hand shape biometric can be considered as a set of methods and techniques used for identifying persons based on their hand silhouette and geometric features, these include and not limited to fingers’ lengths, widths, ratios, angles, etc. These features; among others are extracted from acquired hand images (typically using a camera or a special scanner); the extracted features are compared to stored templates for person identification (Duta, 2009). Research on hand shape biometrics is attractive for the following reasons:

Over the last 30 years, several methods have been proposed in the literature for the hand shape biometric. These include the work of (Sanchez-Reillo, et al., 2000), who extracted several features from hands’ shape such as heights, widths, Deviations, and Angles. Using Gaussian Mixture Their method achieved a 97% accuracy rate when tested on a dataset of 20 persons with 10 hand images for each. (Ma, et al., 2004) showed that B-Spline curves can accurately represent the shape of fingers. (Varchol & Levicky, 2007) employed several geometric features for fingers heights, widths and palm. (Aragonès, 2013) used holistic and geometric features. (Luque-Baena, et al., 2013) used Mutual Information, Genetic algorithm, and Linear Discriminant Analysis (LDA) to achieve high accuracy rates on different hand databases.

A method based on Fourier descriptors and finger area functions were employed by (Kang & Wu, 2014) to achieve high accuracy rates when evaluated on the Bosphorus Hand Database (Bogazici-University, 2015). Other similar works include and not limited to: (Dhole, 2012; Guo, et al., 2012; Basheer & Robinson, 2013; Hassanat, et al., 2015; Amayeh, et al., 2010; Pavešić, et al., 2004) and (Sanchez-Reillo, et al., 2000). Recently, deep features have received a great deal of attention from hand biometric research community, such as the work of (Tarawneh, et al., 2018; Sinha, et al., 2016).

As seen in the literature, hand shape Biometric considering all shapes found in the human hand is a well-researched area. To the best of our knowledge, the identification of a person using one part of the hand; such as the victory sign, has never been investigated in the literature. One exception is the use of hand parts for hand gestures of hand sign language, such as the work of (Fong, et al., 2013). However, such behavioral characteristics of hand gestures, which are designed particularly for hand sign language, will not work well for the identification of terrorists, who are showing only the victory sign, as seen in digital images and videos worldwide.

The goal of this paper is to bridge the gap in this research area by proposing new methods to allow for building a computer system that can identify a terrorist from the shape of their shown fingers, the victory sign in our case. The motivation behind this research is not identifying persons from two fingers, as using two fingers neglects many other discriminative features, knowing that, with more distinctive features we get higher accuracy rates (Dutagac, et al., 2008). Therefore, this research is dedicated for terrorists’ identification, particularly, those who are seen performing the victory sign (v-sign) in digital images, because such a v-sign; might be the only identifying/condemning evidence an investigator can pursue. Moreover, the shape information is also the only feature that can be targeted in such a case, because the hand is normally far away from the camera, and therefore we cannot extract other discriminative fine features of the hand, since these fine minutiae cannot be captured by a normal camera, which is normally used for such images.

Since the number of terrorists’ images that can be downloaded from the media stream is not large, and these images having only one terrorist per each, i.e. each person should provide at least two v-signs, one for training and the other for testing, therefore we opt for creating an in-house victory sign hand image database (VSHI) for the purpose of this study, consisting of the v-signs of 400 different volunteers (male and female). With 10 images per each user, we could train/test our proposed methods. The rest of this paper describes the database used for this study, presents the proposed methods, and presents and discusses the results.

To evaluate the identification process of persons, who were recorded in VSHI we conducted 3 sets of experiments, 1) identification of persons using the invariant Hu moments; 2) identification of persons using the shape of the inner and outer fingers’ contours (HSD); and 3) identification of persons using concatenated features from both methods.

For all experiments in this work, we used the 10-fold cross-validation with different classifiers from the Weka Workbench 3.8 (Frank, et al., 2016) these classifiers include:

We calculate the precision and recall for each experiment, and to reduce ambiguity we calculate the F1-score, which is the harmonic mean of precision and recall

The results of the first experiments set are presented in Table 2.

As can be seen from Table 2 the Hu method performs well with most of the classifiers, this is due to the statistical features that are invariant to rotation, translation, and scale. Other interesting notes include the significant increase in performance when using the LDA, and the very low performance of the SVM. We may contribute these two remarks to the same reason, which is the large number of classes and the small number of examples per class, as the database contains 400 different subjects (classes), with only five examples per subject for each session.

Before starting with the HSD method, we need to determine which number of bins is the best for our experiments? To answer this question we extracted the features with HSD using different number of bins, and since the LDA classifier performed the best in the first set of experiments we used it to identify subjects from both sessions, the accuracy results are presented in Table 3 and Figure 6.

As can be seen from Table 3 and Figure 6 the highest average accuracies recorded when we used 10 and 12 bins, although the accuracy recorded by 10 bins is slightly less than that of the 12 bins, we opt for 10 bins to reduce (dimensionality) number of features. It can be also noted that the identification results from Session 2 were higher than that of Session 1, this might be due to the behavior of the subjects themselves, as they learned how to raise their v-sign consistently the same in session 2.

The second set of experiments focuses on the HSD feature extraction method. To evaluate its discriminative power, again we used the same classifiers with the same parameters. The identification precision, recall and F1-score are recorded in Table 4.

Again, same remarks on the SVM and LDA can be seen from data in Table 4, perhaps for this particular data, the SVM does not work well with Multiclass classification, particularly when we have a very small number of examples per class (5) versus a large number of classes (400). On the contrary, we find that the LDA performs better on Multiclass classification.

Since the LDA performed the best in the previous sets of experiments we used it for the 3rd set of experiments, which used features extracted by both methods; the HU and the HSD. Since the number of features has increased by this merge we opt for dimensionality reduction using principal component analysis (PCA), in addition to data normalization using min/max normalization. The identification results using the LDA classifier are shown in Table 5.

The significant increase in the identification process shown in Table 5 is expected, because the features used come from 2 different methods the HU and the HSD, the first describes the inner geometry of both fingers using the invariant statistical moments, and the second describes the shape of the fingers’ contour, concatenating such features altogether provides more informative description for the learning process. Moreover, when adding both sessions to form one dataset, this increases the number of examples per class and therefore allows for better learning, and this justifies the significant increase in the performance of the identification as can be seen at the last three columns in Table 5.

The effect of normalizing the data on the identification process is obvious in Table 5, this shows that the LDA classifier works better on normalized data. However, this excellent performance comes with an extra cost, which is the increase in the number of features (16 + 40). The dimensionality reduction using PCA preserving 95%, 97%, 98% and 99% of the data variance, succeeded to reduce the data dimensions of the three datasets to 23, 27, 30, and 35 features respectively. At the same time obtained considerable identification performance. For example with only 35 features the F1 score is reduced by less than 2% when testing the 3rd dataset (Hu + HSD on S1 & S2). But when the PCA preserved less than 99% of the data variance the reduction in the identification performance becomes significant, this shows the importance of the features used for the identification process, since the PCA removes some important information that negatively affects the identification process. Nevertheless, PCA still important for dimensionality reduction, particularly when we have a very large number of image features such as the case of face recognition (Oh, et al., 2013) and (Zhao, et al., 1998). Features selection can be applied also to reduce the dimensionality of the training data, but this is out of the scope of this paper.

The novelty of this work stems from its application rather than its methodology and algorithms, since it proves that we can identify a person from the shape of his/her victory sign only. This is important for the application of identifying terrorists, who show their v-sign without showing any other biometric traits. If we hypothetically assume the following scenario:

Using the proposed methods, the detectives can image the suspects’ v-signs and compare them to the one seen in the video, if there is any match they will get some clues to pursue, if no match, they might follow other directions or leave the suspects and take care of other business. Moreover, some well-known terrorists/suspects image their v-sign showing their faces but without killing any, these images can be also used to be compared to the terrorist’s v-sign to identify them.

This work is an extension of a published conference paper about the same subject (Hassanat, et al., 2017), and intends mainly to address the major limitations of our previous work, the main contribution of this work includes:

Our work proposes a new approach for identifying persons, particularly terrorists, from the geometric features of their victory signs alone, as such as sign might be the only information available to identify persons. For this purpose, we created a new image database for the victory sign and made it available for public, and investigated new and old approaches to identify persons from those images. Since we do not have other information like the face of the terrorist to associate the victory sign with, we assume that police or other security agents already having images and hand images for suspects to be associated with the given v-sign of an unknown terrorist.

The proposed methods can be used for person’s identification in general as well, however, in general person’s identification we have stronger pieces of evidence (biometrics) that we can use, such as the whole hand and fingers, face, fingerprint, etc. while in recognizing a terrorist, sometimes the v-sign might be the only available evidence for the identification process.

The excellent results of the conducted experiments show great potential for identifying persons having weak evidence (the shape of the victory sign alone). The experiments also show some limitations of this work, as this work focuses only on geometric features obtained from the 2 fingers that form the v-sign, this was important in this work to avoid the different behavior of the same subject when raising their v-sign. However there is a lot of unused information there, which need to be investigated and added to perhaps enhance the performance of the overall identification process, this neglected information includes and not limited to 1) shape of the palm and wrist, 2) shape of the bent fingers, 3) the different behavior of bending fingers, and 4) skin color and texture. In addition to investigating the problems related to rotation angles in the three dimensions. These limitations will be addressed in the future work using deep learning methods. In addition to doing some feature selection to reduce the dimensionality of the training data.

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