Title: Unconstrained face recognition using deep convolution neural network

Authors: Amrit Kumar Agrawal; Yogendra Narain Singh

Addresses: Apollo Institute of Technology, Sundhela, Sarsaul, Uttar Pradesh, Kanpur 209402, India ' Institute of Engineering and Technology, Dr APJ Abdul Kalam Technical University, Uttar Pradesh, Lucknow 226021, India

Abstract: Different methods have been proposed for face recognition during the past decades that differ essentially on how to determine discriminant facial features for better recognition. Recently, very deep neural networks achieved great success on general object recognition because of their potential in learning capability. This paper presents convolution neural network (CNN)-based architecture for face recognition in unconstrained environment. The proposed architecture is based on a standard architecture of residual network. The recognition performance shows that the proposed framework of CNN achieves the state-of-art performance on publicly available challenging datasets LFW, face94, face95, face96 and Grimace.

Keywords: face recognition; unconstrained environment; deep convolution neural network; CNN.

DOI: 10.1504/IJICS.2020.105183

International Journal of Information and Computer Security, 2020 Vol.12 No.2/3, pp.332 - 348

Received: 21 May 2018
Accepted: 18 Sep 2018
Published online: 14 Feb 2020 *

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