Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS
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.
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- Collobert, R., Bengio, S., and Mariéthoz, J.: Torch: a modular machine learning software library. IDIAP Research Report 02-46 (2002), Martigny, Switzerland. (see also http://www.torch.ch) 914
- Gonzales, R. C., and Woods, R. E.: Digital Image Processing. Addison-Wesley, Reading, Massachusetts, 1993.Google Scholar
- Lüttin, J., and Maître, G.: Evaluation Protocol for the Extended M2VTS Database (XM2VTSDB). IDIAP Communication 98-05 (1998), Martigny, Switzerland.Google Scholar
- Martin, A., Doddington, G., Kamm, T., Ordowski, M., and Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. Proc. Eurospeech’97, 1997, pp. 1895–1898.Google Scholar
- Sanderson, C.: Automatic Person Verification Using Speech and Face Information. PhD Thesis, Griffith University, Brisbane, Australia, 2002.Google Scholar
- Sanderson, C. and Paliwal, K.K.: Polynomial Features for Robust Face Authentication. Proc. International Conf. on Image Processing, Rochester, New York, 2002, pp. 997–1000 (Vol. 3).Google Scholar
- Schalko., R. J.: Pattern recognition: statistical, structural and neural approaches. John Wiley & Sons, USA, 1992.Google Scholar