Pose invariant non-frontal 2D, 2.5D face detection and recognition technique

In this paper, the Pose Invariant Identity Recognition (PIIR) technique is proposed. It measures face resemblance using facial landmarks, which are further vigorous to pose disparity. The proposed technique utilizes the concepts of face frontalization and discriminative learning techniques. 3D Morphable Model is used in both techniques for pose invariant. The proposed technique is validated on CAS-PEAL face dataset. Experimental results signify that the proposed technique attains authentication accuracy of 90.24% on face datasets with 30° pan-angle. The proposed technique is further applied on 2.5D face dataset with variant facial positions and expressions. Results reveal that the proposed method can identify the face accurately. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.