A Preliminary Study of Lower Leg Geometry as a Soft Biometric Trait for Forensic Investigation

Abstract

Criminal and victim identification is always vital in forensic investigation. Many biometric traits, such as DNA, fingerprint, face and palm print, have been regularly used by law enforcement agencies. However, they are not applicable to legal cases where only non-facial body sites of criminals or victims in evidence images are available for identification. These cases include but are not limited to violent protests, masked gunmen and child pornography. To address this challenging identification problem, skin marks, blood vessels hidden in color images, androgenic hair patterns and tattoos have been considered. Tattoos are not always available. Skin marks and blood vessels are suitable for high resolution images. Androgenic hair patterns provide useful identification information even in low resolution images, but their performance is still far from perfect. Thus, new biometric traits are still demanded especially for low resolution evidence images. This paper evaluates lower leg geometry as a soft biometric trait for criminal and victim identification. Lower legs are considered in this study because they are often observable in evidence images. The algorithm utilized in this evaluation first aligns two lower leg shapes from input images and extracts geometric features, including the partial sum of squared difference, the polynomial coefficients and the number of intersection points of the aligned leg shapes. Support vector machines, neural networks and decision trees are used to perform the classification. The algorithm is applied to 1,138 images from 283 subjects. The experimental results indicate that lower leg geometry is an effective soft biometric trait. This study provides a foundation for further research on criminal and victim identification based on body geometry.