Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification

Mandal, Bappaditya; Zhikai, Wang; Li, Liyuan; Kassim, Ashraf A.

Abstract

Face identification (FI) has made significant amount of progress in the last three decades. Its application is now moving towards wearable devices (like Google Glass and mobile devices) leading to the problem of FI on first-person-views (FPV) or ego-centric videos for scenarios like business networking, memory assistance, etc. In the existing literature, performance analysis of various image descriptors on FPV data are little known. In this paper, we evaluate four popular image descriptors: local binary patterns (LBP), scale invariant feature transform (SIFT), local phase quantization (LPQ) and binarized statistical image features (BSIF) and ten different distance measures: Euclidean, Cosine, Chi square, Spearman, Cityblock, Minkowski, Correlation, Hamming, Jaccard and Chebychev with first nearest neighbor (1-NN) and support vector machines (SVM) as classifiers for FI task on both benchmark databases: FERET, AR, GT and FPV database collected using wearable devices like Google Glass (GG). Comparative analysis on these databases using various descriptors shows the superiority of BSIF with Cosine, Chi square and Cityblock distance measures using 1-NN as classifier over other descriptors and distance measures and even some of the current state-of-art benchmark database results.

Citation

Mandal, B., Zhikai, W., Li, L., & Kassim, A. A. (2015). Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification. In Computer Vision - ACCV 2014 Workshops (585-599). https://doi.org/10.1007/978-3-319-16628-5_42

• Conference Name: Computer Vision - ACCV 2014
• Conference Location: Singapore, Singapore
• Start Date: Nov 1, 2014
• End Date: Nov 2, 2014
• Online Publication Date: Apr 12, 2015
• Publication Date: 2015
• Deposit Date: Jun 14, 2023
• Publisher: Springer
• Pages: 585-599
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743; 1611-3349
• Book Title: Computer Vision - ACCV 2014 Workshops
• ISBN: 9783319166278; 9783319166285
• DOI: https://doi.org/10.1007/978-3-319-16628-5_42
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-319-16628-5_42
• Additional Information: First Online: 12 April 2015