Investigating the Impact of Yaw Pose Variation on Facial Recognition Performance

Abstract

Facial recognition systems often struggle with detecting faces in poses that deviate from the frontal view. Therefore, this paper investigates the impact of variations in yaw poses on the accuracy of facial recognition systems and presents a robust approach optimized to detect faces with pose variations ranging from 0◦ to ±90◦ . The proposed system integrates MTCNN, FaceNet, and SVC, and is trained and evaluated on the Taiwan dataset, which includes face images with diverse yaw poses. The training dataset consists of 89 subjects, with approximately 70 images per subject, and the testing dataset consists of 49 subjects, each with approximately 5 images. Our system achieved a training accuracy of 99.174% and a test accuracy of 96.970%, demonstrating its efficiency in detecting faces with pose variations. These findings suggest that the proposed approach can be a valuable tool in improving facial recognition accuracy in real-world scenarios.