3D Ear Identification Using Block-Wise Statistics-Based Features and LC-KSVD

• Published in: IEEE transactions on multimedia Vol. 18; no. 8; pp. 1531 - 1541
• Main Authors: Zhang, Lin, Li, Lida, Li, Hongyu, Yang, Meng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-08-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D ear; Biometrics; Confidence intervals; Dictionaries; dictionary learning; Ear; Encoding; Feature extraction; Histograms; label consistent K-SVD (LC-KSVD); Mathematical analysis; Matlab; Shape; sparse coding; surface types; Three-dimensional displays; Training; Vectors (mathematics)
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2016.2566578

Biometrics authentication has been corroborated to be an effective method for recognizing a person's identity with high confidence. In this field, the use of three-dimensional (3D) ear shape is a recent trend. As a biometric identifier, the ear has several inherent merits. However, although a great deal of efforts have been devoted, there is still large room for improvement in developing a highly effective and efficient 3D ear identification approach. In this paper, we attempt to fill this gap to some extent by proposing a novel 3D ear classification scheme that makes use of the label consistent K-SVD (LC-KSVD) framework. As an effective supervised dictionary learning algorithm, LC-KSVD learns a single compact discriminative dictionary for sparse coding and a multi-class linear classifier simultaneously. To use the LC-KSVD framework, one key issue is how to extract feature vectors from 3D ear scans. To this end, we propose a blockwise statistics-based feature extraction scheme. Specifically, we divide a 3D ear region of interest into uniform blocks and extract a histogram of surface types from each block; histograms from all blocks are then concatenated to form the desired feature vector. Feature vectors extracted in this way are highly discriminative and are robust to mere misalignment between samples. Experiments demonstrate that our approach can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, its computational complexity is extremely low, making it quite suitable for the large-scale identification applications. MATLAB source codes are publicly online available at http://sse.tongji.edu.cn/linzhang/LCKSVDEar/LCKSVDEar. htm.