2015/06/15 by Yang Zhong, Haibo Li, Zhong, Yang +1
Computer Science · Mathematics · #Artificial intelligence #Benchmark (surveying) #Biometric Identification and Security #Biometrics #Block (permutation group theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Face (sociological concept) #Face and Expression Recognition #Face recognition and analysis #Facial recognition system #Feature extraction #Filter (signal processing) #Gabor filter #Identification (biology) #Matching (statistics) #Mathematics #Pattern recognition (psychology) #Set (abstract data type) #cs.CV
paper · pdf · doi:10.48550/arxiv.1506.04655
published in arXiv (Cornell University) (Cornell University)
arxiv created 2015/06/15 · openalex publication_date 2015/06/15 · arxiv updated 2015/06/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/08
Different from face verification, face identification is much more demanding. To reach comparable performance, an identifier needs to be roughly N times better than a verifier. To expect a breakthrough in face identification, we need a fresh look at the fundamental building blocks of face recognition. In this paper we focus on the selection of a suitable signal representation and better matching strategy for face identification. We demonstrate how Gabor phase could be leveraged to improve the performance of face identification by using the Block Matching method. Compared to the existing approaches, the proposed method features much lower algorithmic complexity: face images are only filtered by a single-scale Gabor filter pair and the matching is performed between any pairs of face images at hand without involving any training process. Benchmark evaluations show that the proposed approach is totally comparable to and even better than state-of-the-art algorithms, which are typically based on more features extracted from a large set of Gabor faces and/or rely on heavy training processes.