No intruders - securing face biometric systems from spoofing attacks

The use of face verification systems as a primary source of authentication has been very common over past few years. Better and more reliable face recognition system are coming into existence. But despite of the advance in face recognition systems, there are still many open breaches left in this domain. One of the practical challenge is to secure face biometric systems from intruder’s attacks, where an unauthorized person tries to gain access by showing the counterfeit evidence in front of face biometric system. The face-biometric system having only single 2-D camera is unaware that it is facing an attack by an unauthorized person. The idea here is to propose a solution which can be easily integrated to the existing systems without any additional hardware deployment. This field of detection of imposter attempts is still an open research problem, as more sophisticated and advanced spoofing attempts come into play. In this thesis, the problem of securing the biometric systems from these unauthorized or spoofing attacks is addressed. Moreover, independent multi-view face detection framework is also proposed in this thesis. We proposed three different counter-measures which can detect these imposter attempts and can be easily integrated into existing systems. The proposed solutions can run parallel with face recognition module. Mainly, these counter-measures are proposed to encounter the digital photo, printed photo and dynamic videos attacks. To exploit the characteristics of these attacks, we used a large set of features in the proposed solutions, namely local binary patterns, gray-level co-occurrence matrix, Gabor wavelet features, space-time autocorrelation of gradients, image quality based features. We further performed extensive evaluations of these approaches on two different datasets. Support Vector Machine (SVM) with the linear kernel and Partial Least Square Regression (PLS) are used as the classifier for classification. The experimental results improve the current state-of-the-art reference techniques under the same attach categories