Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication
The lip-region can be interpreted as either a genetic or behavioral biometric trait depending on whether static or dynamic information is used. Despite this breadth of possible application as a biometric, lip-based biometric systems are scarcely developed in scientific literature compared to other more popular traits such as face or voice. This is because of the generalized view of the research community about the lack of discriminative power in the lip region. In this paper, we propose a new method of texture representation called Local Ordinal Contrast Pattern (LOCP) for use in the representation of both appearance and dynamics features observed within a given lip-region during speech production. The use of this new feature representation, in conjunction with some standard speaker verification engines based on Linear Discriminant Analysis and Histogram-distance based methods, is shown to drastically improve the performance of the lip-biometric trait compared to the existing state-of-the-art methods. The best, reported state-of-the-art performance was an HTER of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The improvement obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biométrie modality as opposed to a “soft” biométrie trait as has been done in previous research.