Deep features for efficient multi-biometric recognition with face and ear images

Ibrahim Omara; Gang Xiao; Moussa Amrani; Zifei Yan; Wangmeng Zuo

doi:10.1117/12.2281694

21 July 2017 Deep features for efficient multi-biometric recognition with face and ear images

Ibrahim Omara, Gang Xiao, Moussa Amrani, Zifei Yan, Wangmeng Zuo

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104200D (2017) https://doi.org/10.1117/12.2281694
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Ibrahim Omara, Gang Xiao, Moussa Amrani, Zifei Yan, and Wangmeng Zuo "Deep features for efficient multi-biometric recognition with face and ear images", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200D (21 July 2017); https://doi.org/10.1117/12.2281694

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available