Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Person Identification Using Multimodal Biometrics under Different Challenges

The main aims of this chapter are to show the importance and role of human identification and recognition in the field of human-robot interaction, discuss the methods of person identification systems, namely traditional and biometrics systems, and compare the most commonly used biometric traits that are used in recognition systems such as face, ear, palmprint, iris, and speech. Then, by showing and comparing the requirements, advantages, disadvantages, recognition algorithms, challenges, and experimental results for each trait, the most suitable and efficient biometric trait for human-robot interaction will be discussed. The cases of human-robot interaction that require to use the unimodal biometric system and why the multimodal biometric system is also required will be discussed. Finally, two fusion methods for the multimodal biometric system will be presented and compared

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