On combination of face authentication experts by a mixture of quality dependent fusion classifiers

Abstract

Face as a biometric is known to be sensitive to different factors, e.g., illumination condition and pose. The resultant degradation in face image quality affects the system performance. To counteract this problem, we investigate the merit of combining a set of face verification systems incorporating image-related quality measures. We propose a fusion paradigm where the quality measures are quantised into a finite set of discrete quality states, e.g., "good illumination vs. "bad illumination". For each quality state, we design a fusion classifier. The outputs of these fusion classifiers are then combined by a weighted averaging controlled by the a posteriori probability of a quality state given the observed quality measures. The use of quality states in fusion is compared to the direct use of quality measures where the density of scores and quality are jointly estimated. There are two advantages of using quality states. Firstly, much less training data is needed in the former since the relationship between base classifier output scores and quality measures is not learnt jointly but separately via the conditioning quality states. Secondly, the number of quality states provides an explicit control over the complexity of the resulting fusion classifier. In all our experiments involving XM2VTS well illuminated and dark face data sets, there is a systematic improvement in performance over the baseline method (without using quality information) and the direct use of quality in two types of applications: as a quality-dependent score normalisation procedure and as a quality-dependent fusion method (involving several systems). © Springer-Verlag Berlin Heidelberg 2007.