CONF cardinaux03_avbpa/IDIAP Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS Cardinaux, Fabien Sanderson, Conrad Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-10.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/cardinaux02rr Related documents 4th International Conference on AUDIO- and VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION 10 2003 University of Surrey, Guildford, UK 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. REPORT Cardinaux02RR/IDIAP Face Verification using MLP and SVM Cardinaux, Fabien Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2002/rr-02-21.pdf PUBLIC Idiap-RR-21-2002 2002 IDIAP The performance of machine learning algorithms has steadily improved over the past few years, such as MLP or more recently SVM. In this paper, we compare two successful discriminant machine learning algorithms apply to the problem of face verification: MLP and SVM. These two algorithms are tested on a benchmark database, namely XM2VTS. Results show that a MLP is better than a SVM on this particular task.