3D ear identification using LC-KSVD and local histograms of surface types

In this paper, we propose a novel 3D ear classification scheme, making use of the label consistent K-SVD (LC-KSVD) framework. As an effective supervised dictionary learning algorithm, LC-KSVD learns a compact discriminative dictionary for sparse coding and a multi-class linear classifier simultaneously. To use LC-KSVD, one key issue is how to extract feature vectors from 3D ear scans. To this end, we propose a block-wise statistics based scheme. Specifically, we divide a 3D ear ROI into blocks and extract a histogram of surface types from each block; histograms from all blocks are concatenated to form the desired feature vector. Feature vectors extracted in this way are highly discriminative and are robust to mere misalignment. Experimental results demonstrate that the proposed approach can achieve much better recognition accuracy than the other state-of-the-art methods. More importantly, its computational complexity is extremely low at the classification stage.