Comparison of Face and Ear Based on Multimodal Biometric Identification with Sparse Representation

This paper proposes to employ sparse representation (SR) in multimodal biometric identification of face and ear, and focuses on performance comparison among the presented approaches with different fusion schemes seeking to find guideline for designing mulitimodal biometric recognition systems with sparse representation. In this paper, three multimodal methods are introduced based on the hierarchical multimodal fusion theory and SR's operating mechanism. These methods are MSRCef (multimodal SRC with explicit feature fusion), MSRCif (multimodal SRC with implicit feature fusion), and MSRCs (multimodal SRC at score level). From the viewpoint of multimodal fusion, they adopt different fusion strategies, on the other hand, their major difference lies on the constraint imposed on the sparse representation of face and ear features. Experimental results on three multimodal databases demonstrate that all the three proposed multimodal approaches perform significantly better than those using NN, NFL, SVM, etc. Besides, the proposed multimodal methods are generally comparable, however the method with score level fusion scheme is obviously superior to the others with feature level fusion when the face and/or ear images confront heavy corruption.