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Androgens mediate their functions through androgen receptors (AR). The two triplet repeats in the AR gene (CAG and GGN) are highly polymorphic among various populations and have been extensively studied in diverse clinical conditions and antisocial personality disorders. Several studies have reported either higher levels of testosterone among rapists or the correlation of shorter CAG repeats with criminal activities. However, to date, no study has analyzed AR gene in rapists worldwide, and no study has been conducted on criminals from Indian subcontinent. Therefore, we have analyzed the AR-CAG repeat length in 645 men, of which 241 were convicted for rape, 107 for murder, 26 for both murder and rape, and 271 were control males. The aim was to explore if there was any correlation between CAG repeat length and criminal behavior. The study revealed significantly shorter CAG repeats in the rapists (mean 18.44 repeats) and murderers (mean 17.59 repeats) compared to the control men (mean 21.19 repeats). The criminals who committed murder after rape had a far shorter mean repeat length (mean 17.31 repeats) in comparison to the controls or those convicted of rape or murder alone. In short, our study suggests that the reduced CAG repeats in the AR gene are associated with criminal behavior. This, along with other studies, would help in understanding the biological factors associated with the antisocial or criminal activities.

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In this work, we address off-line signature verification as a writer-independent system. We propose a set of morphological features, extracted from off-line signature images. To examine the effectiveness of the features, a publicly available signature database, namely CEDAR signature database is used. A pair of signatures is fed to the system to give an inference for their (dis)similarity. To get a compact set of features, a multilayer perceptron based feature analysis technique is utilized. A 10-fold cross-validation framework based on support vector machine is used for verification. Receiver operator curve (ROC) analysis gives an equal error rate (EER) of 11.59%, which is comparable to the state-of-the-arts reported on this database.

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