  In this paper, we propose a multimodal verification system integrating face
and ear based on sparse representation based classification (SRC). The face and
ear query samples are first encoded separately to derive sparsity-based match
scores, and which are then combined with sum-rule fusion for verification.
Apart from validating the encouraging performance of SRC-based multimodal
verification, this paper also dedicates to provide a clear understanding about
the characteristics of SRC-based biometric verification. To this end, two
sparsity-based metrics, i.e. spare coding error (SCE) and sparse contribution
rate (SCR), are involved, together with face and ear unimodal SRC-based
verification. As for the issue that SRC-based biometric verification may suffer
from heavy computational burden and verification accuracy degradation with
increase of enrolled subjects, we argue that it could be properly resolved by
exploiting small random dictionary for sparsity-based score computation, which
consists of training samples from a limited number of randomly selected
subjects. Experimental results demonstrate the superiority of SRC-based
multimodal verification compared to the state-of-the-art multimodal methods
like likelihood ratio (LLR), support vector machine (SVM), and the sum-rule
fusion methods using cosine similarity, meanwhile the idea of using small
random dictionary is feasible in both effectiveness and efficiency.
