A face recognition algorithm based on feature fusion. (3rd April 2020)

• Record Type: Journal Article
• Authors: Zhang, Jiwei; Yan, Xiaodan; Cheng, Zelei; Shen, Xueqi
• Other Names: Wu Chase guestEditor.; Yildirim Tulay guestEditor.; Ivanovic Mirjana guestEditor.; Bellatreche Ladjel guestEditor.; Wyrzykowski Roman guestEditor.; Ciorba Florina M. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 14(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-03
• Subjects: deep feature -- face recognition -- feature fusion -- shallow feature -- smart city; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5748
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end‐to‐end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases.