Age-invariant face recognition system using combined shape and texture features

This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase cong...

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Main Authors: Ali, A.S.O., Sagayan, V., Saeed, A.M., Ameen, H., Aziz, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31384 /
Published: Institution of Engineering and Technology 2015
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930330734&doi=10.1049%2fiet-bmt.2014.0018&partnerID=40&md5=d1cad62ac5736adacd77b22e4002c1aa
http://eprints.utp.edu.my/31384/
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spelling utp-eprints.313842022-03-26T03:18:34Z Age-invariant face recognition system using combined shape and texture features Ali, A.S.O. Sagayan, V. Saeed, A.M. Ameen, H. Aziz, A. This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93 when it was evaluated over the FG-NET face aging database. © The Institution of Engineering and Technology 2015. Institution of Engineering and Technology 2015 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930330734&doi=10.1049%2fiet-bmt.2014.0018&partnerID=40&md5=d1cad62ac5736adacd77b22e4002c1aa Ali, A.S.O. and Sagayan, V. and Saeed, A.M. and Ameen, H. and Aziz, A. (2015) Age-invariant face recognition system using combined shape and texture features. IET Biometrics, 4 (2). pp. 98-115. http://eprints.utp.edu.my/31384/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93 when it was evaluated over the FG-NET face aging database. © The Institution of Engineering and Technology 2015.
format Article
author Ali, A.S.O.
Sagayan, V.
Saeed, A.M.
Ameen, H.
Aziz, A.
spellingShingle Ali, A.S.O.
Sagayan, V.
Saeed, A.M.
Ameen, H.
Aziz, A.
Age-invariant face recognition system using combined shape and texture features
author_sort Ali, A.S.O.
title Age-invariant face recognition system using combined shape and texture features
title_short Age-invariant face recognition system using combined shape and texture features
title_full Age-invariant face recognition system using combined shape and texture features
title_fullStr Age-invariant face recognition system using combined shape and texture features
title_full_unstemmed Age-invariant face recognition system using combined shape and texture features
title_sort age-invariant face recognition system using combined shape and texture features
publisher Institution of Engineering and Technology
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930330734&doi=10.1049%2fiet-bmt.2014.0018&partnerID=40&md5=d1cad62ac5736adacd77b22e4002c1aa
http://eprints.utp.edu.my/31384/
_version_ 1741197564442574848
score 11.62408

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