TY - GEN
T1 - Deep or shallow facial descriptors? A case for facial attribute classification and face retrieval
AU - Banaeeyan, Rasoul
AU - Lye, Mohd Haris
AU - Fauzi, Mohammad Faizal Ahmad
AU - Karim, Hezerul Abdul
AU - See, John
N1 - Funding Information:
This research was fully funded by the Ministry of Science, Technology, and Innovation (MOSTI), Malaysia, Project Number 01-02-01-SF0232. We also gratefully acknowledge the feedback from anonymous reviewers.
Publisher Copyright:
© Springer International Publishing AG 2017.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/3/16
Y1 - 2017/3/16
N2 - With the largely growing quantity of face images in the social networks and media, different face analyzing systems are developed to be employed in real-world situations such as face recognition, facial expression detection, or automated face tagging. Two demanding face-related applications are studied in this paper: facial attribute classification and face image retrieval. The main common issue with most of the attribute classifiers and face retrieval systems is that they fail to perform well under various facial expressions, pose variations, geometrical deformation, and photometric alterations. On one hand, the emerging role of deep CNNs (convolutional neural networks) has shown superior results in tasks like object recognition, face recognition, etc. On the other hand, their applications are yet to be more investigated in facial attribute classification and face retrieval. In this study, we compare the performance of shallow and deep facial descriptors in the two mentioned applications by proposing to exploit distinctive facial features from a very deep pre-trained CNN for attribute classification as well as constructing deep attributedriven feature vectors for face retrieval. According to the results, the higher accuracy of the attribute classifiers and superior performance of the face retrieval system is demonstrated.
AB - With the largely growing quantity of face images in the social networks and media, different face analyzing systems are developed to be employed in real-world situations such as face recognition, facial expression detection, or automated face tagging. Two demanding face-related applications are studied in this paper: facial attribute classification and face image retrieval. The main common issue with most of the attribute classifiers and face retrieval systems is that they fail to perform well under various facial expressions, pose variations, geometrical deformation, and photometric alterations. On one hand, the emerging role of deep CNNs (convolutional neural networks) has shown superior results in tasks like object recognition, face recognition, etc. On the other hand, their applications are yet to be more investigated in facial attribute classification and face retrieval. In this study, we compare the performance of shallow and deep facial descriptors in the two mentioned applications by proposing to exploit distinctive facial features from a very deep pre-trained CNN for attribute classification as well as constructing deep attributedriven feature vectors for face retrieval. According to the results, the higher accuracy of the attribute classifiers and superior performance of the face retrieval system is demonstrated.
UR - http://www.scopus.com/inward/record.url?scp=85016106521&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54427-4_32
DO - 10.1007/978-3-319-54427-4_32
M3 - Conference contribution
AN - SCOPUS:85016106521
SN - 9783319544267
T3 - Lecture Notes in Computer Science
SP - 434
EP - 448
BT - Computer Vision. ACCV 2016
A2 - Chen, Chu-Song
A2 - Lu, Jiwen
A2 - Ma, Kai-Kuang
PB - Springer
T2 - 13th Asian Conference on Computer Vision 2016
Y2 - 20 November 2016 through 24 November 2016
ER -