Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit

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