A Combination of Spatiotemporal ICA and Euclidean Features for Face Recognition

ICA decomposes a set of features into a basis whose components are statistically independent. It minimizes the statistical dependence between basis functions and searches for a linear transformation to express a set of features as a linear combination of statistically independent basis functions. Though ICA has found its application in face recognition, mostly spatial ICA was employed. Recently, we studied a joint spatial and temporal ICA method, and compared the performance of different ICA approaches by using our special face database collected by AcSys FRS Discovery system. In our study, we have found that spatiotemporal ICA apparently outperforms spatial ICA, and it can be much more robust with better performance than spatial ICA. These findings justify the promise of spatiotemporal ICA for face recognition. In this paper we report our progress and explore the possible combination of the Euclidean distance features and the ICA features to maximize the success rate of face recognition.