Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

Abstract

We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability. Apart from structural differences, the two approaches use different training criteria; the MLP approach uses a discriminative criterion, while the GMM approach uses a combination of Maximum Likelihood (ML) and Maximum a Posteriori (MAP) criteria. Experiments on the XM2VTS database show that for low resolution faces the MLP approach has slightly lower error rates than the GMM approach; however, the GMM approach easily outperforms the MLP approach for high resolution faces and is significantly more robust to imperfectly located faces. The experiments also show that the computational requirements of the GMM approach can be significantly smaller than the MLP approach at a cost of small loss of performance.

The authors thank the Swiss National Science Foundation for supporting this work through the National Center of Competence in Research (NCCR) on “Interactive Multimodal Information Management (IM2)”. This work was also funded by the European projects “BANCA and CIMWOS”, through the Swiss Federal Office for Education and Science (OFES).