Title: Fusion of periocular and forehead features for masked face recognition

Authors: Diwakar Agarwal; Atul Bansal

Addresses: Department of Electronics and Communication Engineering, GLA University, Mathura, Uttar Pradesh, India ' Department of Electronics and Communication Engineering, GLA University, Mathura, Uttar Pradesh, India

Abstract: Most of the automated face recognition systems are not able to identify authorised users with masked faces. This paper proposed a face recognition system based on the fusion of information obtained from mostly uncovered regions of the masked face, i.e., periocular and forehead. Deep features are extracted from the periocular region by using the pre-trained inception-v3 model, while the handcrafted features such as local binary pattern (LBP) and histogram of oriented gradients (HOG) are extracted for the forehead region. The performance of the proposed method is evaluated on Georgia Tech Face Database (GTDB), Color FERET face image database, and self-acquired masked face database. Experimental results show that the fusion of periocular with forehead LBP and HOG features achieved notable verification and recognition rates as GTDB - 76.39% and 52%, respectively, Color FERET database - 96.63% and 95.62%, respectively, and self-acquired masked face database - 89.95% and 67%, respectively.

Keywords: biometrics; forehead; fusion; masked face; multimodal; periocular.

DOI: 10.1504/IJCVR.2026.155532

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.2, pp.133 - 158

Accepted: 14 Nov 2023
Published online: 05 Aug 2026 *

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