Class Dependent Kernel Discrete Cosine Transform Features for Enhanced Holistic Face Recognition in FRGC-II
Marios Savvides, Jingu Heo, Ramzi Abiantun, Chunyan Xie, B. V. K. Vijaya Kumar · 2006
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.