An Efficient Pre-processing and Transfer Learning-Based Deep-Network for Face Anti-Spoofing - R. J. Bhiwani, Avinash B. Lambat (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Sophisticated and enhanced image editing tools have created chaos in the modern world and are intended to fool the best recognition systems. Many such tools are available for spoofing images which has made the authority’s task miserable. Anti-social elements with such sophisticated image and video spoofing packages and advanced hardware tools have made the antispoofing mechanism a complex task. The work introduced in this paper for face-antispoofing uses an efficient pre-processing framework followed by deep-network-based feature extraction and classification. The complete model can discriminate between real and spoofed faces under uneven illumination conditions. The first part of the model enhances the face details for better quality features. The YOLOV5 network extracts the features and classifies the faces as the real and the spoof ones using a transfer learning approach. The work considered images subjected to Replay Attack from a well-known dataset and obtained an accuracy of about 99%. Experimental analysis of imbalanced data showed that the proposed face-antispoofing model performed better than other state-of-the-art work found in the literature.