Multi-Pose Face Recognition Using Pairwise Supervised Dictionary Learning

Article type: Research Article

Authors: Farahani, Ali | Mohseni, Hadis^{; *}

Affiliations: Department of Computer Engineering, Shahid Bahonar University of Kerman, Pazhouhesh Square, Kerman, Iran. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author.

Abstract: A major challenge in face recognition is handling large pose variations. Here, we proposed to tackle this challenge by a three step sparse representation based method: estimating the pose of an unseen non-frontal face image, generating its virtual frontal view using learned view-dependent dictionaries, and classifying the generated frontal view. It is assumed that for a specific identity, the representation coefficients based on the view dictionary are invariant to pose and view-dependent frontal view generation transformations are learned based on pair-wise supervised dictionary learning. Experiments conducted on FERET and CMU-PIE face databases depict the efficacy of the proposed method.

Keywords: face recognition, multi-pose, sparse representation, supervised dictionary learning

Journal: Informatica, vol. 30, no. 4, pp. 647-670, 2019