Class Dependent Kernel Discrete Cosine Transform Features for Enhanced Holistic Face Recognition in FRGC-II

Face recognition is one of the least intrusive biometric modalities that can be used to identify individuals from surveillance video. In such scenarios the users are under the least co-operative conditions and thus the ability to perform robust face recognition in such scenarios is very challenging. In this paper we focus on improving the face recognition performance on a large database with over 36,000 facial images from the Face Recognition Grand Challenge Phase-II data collected by University of Notre Dame. We particularly focus on Experiment 4 which is the most challenging and captured in uncontrolled conditions where the baseline PCA algorithm yields 12% verification rate at 0.1% FAR. We propose a novel approach using class-dependent kernel discrete cosine transform features which improves the performance significantly yielding a 91.33% verification rate at 0.1% FAR, and we also show that by working in the DCT transform domain for obtaining non-linear features is more optimal than working in the original spatial-pixel domain which only yields a verification rate of 85% at 0.1% FAR. Thus our proposed method outperforms the baseline by 79.33% in verification rate @0.1% False Acceptance Rate.