B3D-EAR: Binarized 3D descriptors for ear-based human recognition

Traditional 3D feature descriptors often utilize real-valued vectors, posing challenges in terms of computational complexity and space constraints during matching. This study introduces a novel approach for generating binary 3D feature descriptors using correntropy, an online estimate of Renyi’s quadratic entropy. By employing this technique, the efficiency of state-of-the-art 3D descriptors is significantly enhanced. The effectiveness of the proposed methodology is demonstrated through evaluations on two datasets, UND-J2 and an in-house dataset, comprising 3D scans of human ears. Efficient algorithms for matching keypoints and their binary descriptors are employed, addressing both space and time complexities. The matching process is assessed using distance metrics and iterative closest point (ICP) alignment. Experimental results reveal that the method achieves a recognition performance of 98.62% with an equal error rate (EER) of 1.54%, comparable to state-of-the-art techniques. This highlights the efficacy of the method in generating binary descriptors without compromising accuracy. •A novel binary 3D descriptor for object matching is introduced reducing time & space.•The method utilizes the system identification concept to binarize the feature vectors.•Approach has been verified in human recognition applications using 3D ear biometrics.•Approach performs well on a big database, comparable to SOTA real-valued descriptors.