A two-phase approach for expression invariant 3D face recognition using fine-tuned VGG-16 and 3D-SIFT descriptors

Expression invariant 3D face recognition systems have many computer vision applications such as human-computer interaction. Most 3D face recognition systems rely on rigid region features and a substantial amount of training data to achieve better accuracy. However, the computational cost of these systems is very high. In order to address the problem of compromising computational efficiency for accuracy, this paper presents a computationally efficient two-phase expression invariant 3D face recognition approach using fine-tuned VGG-16 and 3D-SIFT descriptors. In the first phase, the pre-trained VGG-16 is fine-tuned with the Texas database and the CASIA-3D database. The candidates are recognized using the fused features from the fine-tuned VGG-16 and landmarks-based angles. In the second phase, the 3D-SIFT descriptors are computed on the rigid component of the probe and candidate 3D faces. Then, the final identity is obtained based on the best 3D-SIFT keypoints’ match score. Reporting competitive results in comparison to the state-of-the-art, the proposed method achieves 100% and 97.69% recognition rates respectively for the neutral-neutral and neutral-non neutral scenarios on the Bosphorus Database. Further, it requires only 1.27 seconds to identify a probe from a gallery with 105 faces.