CEVAB: NIR-VIS face recognition using convolutional encoder-based visual attention block
Patil Jayashree Madhukar, P.M. Ashok Kumar, R. Anitha · International Journal of Data Analysis Techniques and Strategies · 2024
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.