Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition. (17th June 2019)

• Record Type: Journal Article
• Authors: Ratyal, Naeem; Taj, Imtiaz Ahmad; Sajid, Muhammad; Mahmood, Anzar; Razzaq, Sohail; Dar, Saadat Hanif; Ali, Nouman; Usman, Muhammad; Baig, Mirza Jabbar Aziz; Mussadiq, Usman
• Other Names: Smolka Bogdan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-06-17
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/3547416
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.