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Proceedings Paper

Deep features for efficient multi-biometric recognition with face and ear images
Author(s): Ibrahim Omara; Gang Xiao; Moussa Amrani; Zifei Yan; Wangmeng Zuo
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Paper Abstract

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200D (21 July 2017); doi: 10.1117/12.2281694
Show Author Affiliations
Ibrahim Omara, Harbin Institute of Technology (China)
Menoufia Univ. (Egypt)
Gang Xiao, No. 211 Hospital of PLA (China)
Moussa Amrani, Harbin Institute of Technology (China)
Univ. of Mentouri (Algeria)
Zifei Yan, Harbin Institute of Technology (China)
Wangmeng Zuo, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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