Effects of pose and image resolution on automatic face recognition. Issue 2 (1st June 2016)

• Record Type: Journal Article
• Authors: Mahmood, Zahid; Ali, Tauseef; Khan, Samee U.
• Is Part Of: IET biometrics. Volume 5:Issue 2(2016)
• Page Start: 111
• Page End: 119
• Publication Date: 2016-06-01
• Subjects: face recognition -- image resolution -- biometrics (access control) -- security -- image classification -- principal component analysis -- learning (artificial intelligence); image resolution -- automatic face recognition -- face recognition systems -- biometric access control -- security -- law enforcement -- baseline face recognition algorithms -- adaptive boosting -- AdaBoost -- linear discriminant analysis -- principal component analysis -- PCA‐based approach -- local binary pattern -- LBP‐based approach -- systematic pose variation -- illumination -- low‐resolution images; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2015.0008
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : The popularity of face recognition systems have increased due to their use in widespread applications. Driven by the enormous number of potential application domains, several algorithms have been proposed for face recognition. Face pose and image resolutions are among the two important factors that influence the performance of face recognition algorithms. In this study, the authors present a comparative study of three baseline face recognition algorithms to analyse the effects of two aforementioned factors. The algorithms studied include (a) the adaptive boosting (AdaBoost) with linear discriminant analysis as weak learner, (b) the principal component analysis (PCA)‐based approach, and (c) the local binary pattern (LBP)‐based approach. They perform an empirical study using the images with systematic pose variation and resolution from multi‐pose, illumination, and expression database to explore the recognition accuracy. This evaluation is useful for practical applications because most engineers start development of a face recognition application using these baseline algorithms. Simulation results revealed that the PCA is more accurate in classifying the pose variation, whereas the AdaBoost is more robust in identifying low‐resolution images. The LBP does not classify face images of size 20 × 20 pixels and below and has lower recognition accuracy than PCA and AdaBoost.