Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition
Joint Authors
Ali, Nouman
Ratyal, Naeem
Taj, Imtiaz Ahmad
Sajid, Muhammad
Mahmood, Anzar
Razzaq, Sohail
Dar, Saadat Hanif
Usman, Muhammad
Baig, Mirza Jabbar Aziz
Mussadiq, Usman
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-17
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ratyal, Naeem& Taj, Imtiaz Ahmad& Sajid, Muhammad& Mahmood, Anzar& Razzaq, Sohail& Dar, Saadat Hanif…[et al.]. 2019. Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195190
Modern Language Association (MLA)
Ratyal, Naeem…[et al.]. Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition. Mathematical Problems in Engineering No. 2019 (2019), pp.1-21.
https://search.emarefa.net/detail/BIM-1195190
American Medical Association (AMA)
Ratyal, Naeem& Taj, Imtiaz Ahmad& Sajid, Muhammad& Mahmood, Anzar& Razzaq, Sohail& Dar, Saadat Hanif…[et al.]. Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195190
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195190