Abstract
Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D.
Highlights
Face recognition is widely used in video, passports, security, psychological research, automatic aid, robotics, fatigue testing, human-machine interfaces, and other occasions
Due to the good representation of Anthropometric characteristics and Curvelet feature on the surface information, we extract the left eye, right eye, nose, and mouth region from the face as the local feature region for the 3D face recognition according to the Anthropometric characteristics
The experiment result shows that the proposed Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (ACFFR) has fused 3D Curvelet features on the feature region based on Anthropometric features
Summary
Face recognition is widely used in video, passports, security, psychological research, automatic aid, robotics, fatigue testing, human-machine interfaces, and other occasions. Due to the good representation of Anthropometric characteristics and Curvelet feature on the surface information, we extract the left eye, right eye, nose, and mouth region from the face as the local feature region for the 3D face recognition according to the Anthropometric characteristics. The Anthropometric characteristic was used to extract the nose, mouth, left eye, and right eye feature region in the proposed ACFFR algorithm. It has better robustness for the changes of expression and illumination compared with the method proposed by Elaiwat et al [9]. In order to verify the performance, the proposed ACFFR algorithm is tested in the famous Texas 3D database [12] and compared with the Anthroface3D and Curveletface3D face recognition methods. Experimental results show that the proposed ACFFR algorithm has better robustness in expression, light, and other environmental changes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
