A further study of low resolution androgenic hair patterns as a soft biometric trait

Soft biometric traits such as skin color, tattoos, shoe size, height, and weight have been regularly used for forensic investigation, especially when hard biometric traits, e.g., faces and fingerprints are not available. Recently, a new soft biometric trait, androgenic hair also called body hair, was evaluated. The previous study showed that low resolution androgenic hair patterns have potential for forensic investigation. However, it was believed that they are not a distinctive biometric trait because of the reported accuracy. To explore discriminative information in androgenic hair patterns, in this paper, 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 grid systems to increase discriminative power and robustness, and class-specific partial least squares (PLS) models to utilize the features effectively, is employed. To further enhance the performance of the class-specific PLS models trained on very limited positive samples, one to three images per model in the experiments, and further enhance robustness against viewpoint and pose variations, a scheme is designed to generate more positive samples from a single image. Experimental results on 1493 low resolution leg images with large viewpoint and pose variations from 412 legs demonstrate that low resolution androgenic hair patterns contain rich information and the impression of low discriminative power on androgenic hair is due to the method used in the previous study.