Minimizing Number of Distinct Poses for Pose-Invariant Face Recognition

Carter Ung, Pranav Mantini, Shishir K. Shah

Abstract: In unconstrained environments, extreme pose variations of the face are a long-standing challenge for person identification systems. The natural occlusion of necessary facial landmarks is notable to model performance degradation in face recognition. Pose-invariant models are data-hungry and require large variations of pose in training data to achieve comparable accuracy in recognizing faces from extreme viewpoints. However, data collection is expensive and time-consuming, resulting in a scarcity of facial datasets with large pose variations for model training. In this study, we propose a training framework to enhance pose-invariant face recognition by identifying the minimum number of poses for training deep convolutional neural network (CNN) models, enabling higher accuracy with minimum cost for training data. We deploy ArcFace, a state-of-the-art recognition model, as a baseline to evaluate model performance in a probe-gallery matching task across groups of facial poses categorized