CEVAB: NIR-VIS face recognition using convolutional encoder-based visual attention block

Patil Jayashree Madhukar, P.M. Ashok Kumar and R. Anitha

International Journal of Data Analysis Techniques and Strategies, 2024, vol. 16, issue 3, 262-281

Abstract: Recent research in night vision face recognition has spiked due to the rise of night-time surveillance in public areas, where cameras often use near infrared (NIR) images. This paper presents a new face recognition method, the convolutional encoder-based visual attention block (CEVAB), optimised for NIR and visible spectrum (VIS) images. CEVAB combines a convolutional encoder with an attention-based architecture, focusing on critical facial features to enhance accuracy against watchlists. Tested on the FaceSurv dataset with over 132,000 images, CEVAB outshines traditional methods in VIS, achieving 95.08% Rank 1 accuracy at close distances, and in NIR, with 74.00% Rank 1 accuracy, surpassing competitors like Verilook and ResNet-50. These results prove CEVAB's exceptional adaptability and performance in various imaging conditions, significantly advancing night vision face recognition technology.

Keywords: deep learning; face recognition; NIR images; visual attention; convolutional decoder; convolutional encoder; cross-spectral recognition; deep learning; face recognition; feature extraction; night vision; NIR-VIS; surveillance systems; visual attention. (search for similar items in EconPapers)
Date: 2024
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