Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion

• Published in: CVPR 2011 WORKSHOPS pp. 1 - 8
• Main Authors: Di Huang, Ben Soltana, Wael, Ardabilian, M., Yunhong Wang, Liming Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2011
• Subjects: Face; Face recognition; Lighting; Neurons; Solid modeling; Three dimensional displays
• ISBN: 9781457705298; 145770529X
• ISSN: 2160-7508; 2160-7516
• DOI: 10.1109/CVPRW.2011.5981672

This paper proposes a novel biological vision-based facial description, namely Perceived Facial Images (PFIs), aiming to highlight intra-class and inter-class variations of both facial range and texture images for textured 3D face recognition. These generated PFIs simulate the response of complex neurons to gradient information within a certain neighborhood and possess the properties of being highly distinctive and robust to affine illumination and geometric transformation. Based on such an intermediate facial representation, SIFT-based matching is further carried out to calculate similarity scores between a given probe face and the gallery ones. Because the facial description generates a PFI for each quantized gradient orientation of range and texture faces, we then propose a score level fusion strategy which optimizes the weights using a genetic algorithm in a learning step. Evaluated on the entire FRGC v2.0 database, the rank-one recognition rate using only 3D or 2D modality is 95.5% and 95.9%, respectively; while fusing both modalities, i.e. range and texture-based PFIs, the final accuracy is 98.0%, demonstrating the effectiveness of the proposed biological vision-based facial description and the optimized weighted sum fusion.