Behavioral Consistency Extraction for Face Verification

In this paper we investigate how the use of computational statistical models, derived from moving images, can take part in the face recognition process. As a counterpart to psychological experimental results showing a significant beneficial effect of facial non-rigid movement, two features obtained from face sequences, the central tendency and type of movement variation, are associated to improve face verification compared with single static images. By using General Group-wise Registration algorithm, the correspondences across the sequences are captured to build a combined shape and appearance model, parameterizing the face sequences. The parameters are projected to an identity-only space to find the central tendency of each subject. In addition, facial movement consistencies across different behaviors exhibited by the same subjects are recorded. These two features are fused by a confidence-based decision system for authentication applications. Using the BANCA video database, the results show that the extra information extracted from moving images significantly and efficiently improves performance.