RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking. Issue 9 (25th November 2020)

• Record Type: Journal Article
• Authors: Lei, Zhenfeng; Zhang, Xiaoying; Yang, Shuangyuan; Ren, Zihan; Akindipe, Olusegun F.
• Is Part Of: Enterprise information systems. Volume 14:Issue 9/10(2020)
• Page Start: 1379
• Page End: 1393
• Publication Date: 2020-11-25
• Subjects: Face recognition -- visual tracking -- Deep Learning -- cognitive computing; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2019.1668964
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: In this paper, we proposed a hybrid method based on Deep Learning (DL) and visual tracking, RFR-DLVT, to achieve effective face recognition (FR). First, video sequences are divided into reference frames (RFs) and non-reference frames (NRFs). Then, the target face is identified through the DL-based FR method in RFs. In the meantime, the Kernelized -correlation-filters-based visual tracking method is used in NRFs to speed up FR. Our proposed method is tested on common data sets and achieves better performance. Particularly, RFR-DLVT has an accuracy of 99.6% and an efficiency of 30 (FPS) in the real-time FR on real-life surveillance videos.