Incremental fusion of partial biometric information

Abstract

Existing face recognition schemes are mostly based on extracting biometric feature vectors either from whole face images, or from a fixed facial region (e.g., eyes, nose, and mouth). Extreme variation in quality conditions between biometric enrolment and verification stages badly affects the performance of face recognition systems. Such problems have partly motivated several investigations into the use of partial facial features for face recognition. Nevertheless, partial face recognition is potentially useful in several applications, for instance, it used in forensics for detectives to identify individuals after some accidents such as fire or explosion. In this paper, we propose a scheme to fuse the biometric information of partial face images incrementally based on their recognition accuracy (or discriminative power) ranks. Such fusion scheme uses the optimal ratio of full/partial face images in each different quality condition. We found that such scheme is also useful for full face images to enhance authentication accuracy significantly. Nevertheless, it reduces the required storage requirements and processing time of the biometric system. Our experiments show that the required ratio of full/partial facial images to achieve optimal performance varies from (5%) to (80%) according to the quality conditions whereas the authentication accuracy improves significantly for low quality biometric samples.