Robust Facial Biometrics using Multi-Spectral Imaging

Face recognition systems have witnessed a noticeable improvement in the performance accuracy in constrained environmental conditions attributed with favorable illumination conditions and specific working distance. The robustness of face recognition system has been seriously challenged when these systems are operated with unfavorable imaging conditions in realistic mode, resulting in to poor performance of this system. With the recent advancement in the sensing technology, and availability at affordable price, have allowed to build face recognition system to work beyond visible spectra in particularly InfraRed spectrum to improve the performance of the biometric systems under heterogeneous working conditions. Specifically, multi-spectral face recognition have received significant attention due to its imaging ability to collect discriminative spatial and spectral detail information across the wide electromagnetic spectrum. This thesis, focuses on exploring the significance of multi-spectral imaging platform for improved performance of facial biometrics. We first develop the custom built multi-spectral imaging solution operated with nine narrow spectral bands in discrete and disjoint manner by exploiting visible and Near-Infra-Red spectrum. Further, we present the proposed method based on Grassmann Mannifold Projection Metric Learning (GPML) to fully exploit the spectral information in holistic manner. Considering the difference in photometric reflectance and/or emittance information of individuals and its variation across different age groups, this work present experimental evaluation of various state-of-the-art feature extraction method on the spectral face database collected across two different age groups (below 15 years and above 20 years) using multispectral imaging platform to test the accuracy of algorithms. Similarly, using the inherent properties of multi-spectral imaging, we present the gender classification on the spectral database collected in six different illumination conditions including five in indoor scenario and one in outdoor uncontrolled manner. Further, gender classification is performed using two proposed frameworks, based on extracting spectral band features with Spectral Angle Mapper and Affine Hull methods. We then present the vulnerability of multi-spectral face imaging against disguise attacks. Specifically, we explore the challenging disguise attack corresponding to identity concealer using disguise accessories such as normal beard and long beard. To this end, we present presentation attack detection technique by exploring spectral signature. Finally, we explore the applicability of multi-spectral imaging for periocular matching. We present the individual spectral bands performance comprehensively using state-of-theart methods for periocular recognition and subsequent study to perform cross spectral periocular matching to analyze the performance accuracy due to difference in the spectral modality. Additionally, during the due course of this thesis work, a number of multi-spectral face database collected, are made available in the public repository freely for reproducible research.