Biometric identification using augmented database

Androgenic hair pattern is one of the newest soft biometric trait that can be used to identify criminals when their faces are covered in the evidences of criminal investigation. In real-life situation, sometimes the available evidence is limited thus creating problems for authorities to identify criminal based on the limited data. This research developed the recognition system to identify individuals based on their androgenic hair pattern in a limited data situation in such a way that the limited images were expanded by the augmentation process. There were 50 images studied and expanded into 2.000 images from the augmentation process of rotating, reflecting, adjusting color and intensity. Furthermore, the effect of human skin color extraction was investigated by employing HSV and YCbCr color spaces. The scale-space hierarchy was built among the images with Gaussian function and produced 70% recognition precision that was around more than 2 times higher compared to system of recognition with only limited data

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.MOE (Min. of Education, S’pore)Accepted versio

Skin Texture as a Source of Biometric Information

Traditional face recognition systems have achieved remarkable performances when the whole face image is available. However, recognising people from partial view of their facial image is a challenging task. Face recognition systems' performances may also be degraded due to low resolution image quality. These limitations can restrict the practicality of such systems in real-world scenarios such as surveillance, and forensic applications. Therefore, there is a need to identify people from whatever information is available and one of the possible approaches would be to use the texture information from available facial skin regions for the biometric identification of individuals. This thesis presents the design, implementation and experimental evaluation of an automated skin-based biometric framework. The proposed system exploits the skin information from facial regions for person recognition. Such a system is applicable where only a partial view of a face is captured by imaging devices. The system automatically detects the regions of interest by using a set of facial landmarks. Four regions were investigated in this study: forehead, right cheek, left cheek, and chin. A skin purity assessment scheme determines whether the region of interest contains enough skin pixels for biometric analysis. Texture features were extracted from non-overlapping sub-regions and categorised using a number of classification schemes. To further improve the reliability of the system, the study also investigated various techniques to deal with the challenge where the face images may be acquired at different resolutions to that available at the time of enrolment or sub-regions themselves be partially occluded. The study also presented an adaptive scheme for exploiting the available information from the corrupt regions of interest. Extensive experiments were conducted using publicly available databases to evaluate both the performance of the prototype system and the adaptive framework for different operational conditions, such as level of occlusion and mixture of different resolution skin images. Results suggest that skin information can provide useful discriminative characteristics for individual identification. The comparison analyses with state-of-the-art methods show that the proposed system achieved a promising performance