Pose-Invariant Face Recognition Using Optimized Hybrid Yolo Algorithm from Image

Abstract

One application of computer vision is face recognition, which essentially involves the identification of visual patterns. Face recognition is a tool that we often employ for multimedia management, smart card applications, justice reform, and security. The goal of a face recognition system is to automatically identify faces in any image or video using a computer vision domain. There are several methods for the detection of faces from the video; still, the inaccurate detection and computational complexity degrades the recognition precision. Hence, an optimized hybrid deep learning is introduced for the recognition of pose-invariant faces from the image. The pose-invariant face recognition is employed using the proposed ResNet-152 integrated YOLO (Res-YOLONet), wherein the ResNet-152 and YOLOv5 are hybridized together to enhance the recognition accuracy with minimal computational complexity. Besides, the loss function optimization is devised using the proposed Enhanced Fennec Fox (EnFF) algorithm. The proposed EnFF algorithm is designed by integrating the adaptive weighting strategy within the conventional Fennec Fox algorithm for acquiring the global best solution. The loss function optimization using the EnFF enhances the recognition accuracy. The assessment of the proposed EnFF_Res-YOLONet based on Accuracy, Precision, Recall, and Specificity acquires the values of 96%, 94%, 97%, and 96.6%, respectively.