Title: Real-time single-view face detection and face recognition based on aggregate channel feature

Authors: Michael George; Aswathy Sivan; Babita Roslind Jose; Jimson Mathew

Addresses: Division of Electronics and Communication Engineering, School of Engineering, Cochin University of Science and Technology, Kochi 682022, India ' Division of Electronics and Communication Engineering, School of Engineering, Cochin University of Science and Technology, Kochi 682022, India ' Division of Electronics and Communication Engineering, School of Engineering, Cochin University of Science and Technology, Kochi 682022, India ' Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna 801103, India

Abstract: A single-view face detector and a novel face recognition method based on the aggregate channel feature (ACF) that work at real-time speeds, suitable in a computing resource-constrained setting are presented in this work. The four stage tree-based face detector is trained on a subset of the AFLW dataset. The face detection performance is analysed using the AFW dataset. The face recogniser uses ACF features along with classification algorithms, either SVM or MLP. The face recogniser is trained and tested on the GATech Face dataset. Our face detector displays comparable performance against the state of the art while working at 29.8 fps. The face recogniser achieves a level of performance that is competitive with other state of the art works. The effect of PCA-based dimension reduction of ACF features on face recognition performance is also studied in this work.

Keywords: aggregate channel feature; ACF; support vector machine; SVM; multi-layer perceptron; MLP; face recognition; face detection.

DOI: 10.1504/IJBM.2019.100829

International Journal of Biometrics, 2019 Vol.11 No.3, pp.207 - 221

Available online: 23 May 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article