No abstract

Criminal and victim identification is always vital in forensic investigation. Many biometric traits, such as DNA, fingerprint, face and palmprint, 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.

No abstract

Criminal and victim identification is always important in forensic investigation. However, it can be a very challenging problem for identifying criminals and victims in digital media when only their non-facial body sites are available in evidence images. These criminals and victims can be masked gunmen, paedophiles, and victims in child pornographic and voyeur images.To solve the above problem, several novel alignment and identification approaches are proposed in this thesis. Firstly, lower leg geometry is proposed as a soft biometric trait for criminal and victim identification. This study provides a foundation for further research based on body geometry. Secondly, leg geometry and hair follicles are proposed to align the androgenic hair patterns in consideration of viewpoint and pose variations, which were ignored by a recent paper suggesting androgenic hair patterns for identification.Experiments on 1,138 high and low resolution images from 283 different legs show that the proposed alignment algorithms provide improvements of 5%-10% on different experimental settings.Thirdly, a new approach is developed to improve the identification of androgenic hair patterns significantly. In the past, it was believed that androgenic hair patterns in low resolution images are not a distinctive biometric trait because of the previous result. A new algorithm, which makes use of leg geometry to align lower leg images, large feature sets (about 60,000 features) extracted through multi-directional gridding systems to increase discriminative power and robustness, the partial least squares (PLS) method to handle imbalanced training data and to perform the multi-grid feature fusion, and scheme I thank my fellow labmates in Biometrics and