The paper presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000 [14]. Participants had to use identical data sets from a large, publicly available multimodal database XM2VTSDB. Training and evaluation was carried out according to an a priori known protocol ([7]). Verification results of all tested algorithms have been collected and made public on the XM2VTSDB website [15], facilitating large scale experiments on classifier combination and fusion. Tested methods included, among others, representatives of the most common approaches to face verificationelastic graph matching, Fisher's linear discriminant and Support vector machines.
The paper studies Support Vector Machines (SVMs) in the context of face verification and recognition. Our study supports the hypothesis that the SVM approach is able to extract the relevant discriminatory information from the training data and we present results showing superior performance in comparison with benchmark methods. However, when the representation space already captures and emphasises the discriminatory information (e.g. Fisher's linear discriminant), SVMs loose their superiority. The results also indicate that the SVMs are robust against changes in illumination provided these are adequately represented in the training data. The proposed system is evaluated on a large database of 295 people obtaining highly competitive results: an equal error rate of 1 for verification and a rank-one error rate of 2 for recognition (or 98 correct rank-one recognition).
We propose a method for fast face localisation and verification (identification) based on a robust form of correlation. Geometric and photometric normalisation of face images is achieved by direct minimisation. During optimisation, the correlation is estimated from a set of samples drawn from a Sobol sequence. This Monte-Carlo technique speeds the evaluation of correlation approximately twenty five times and makes the optimisation process near-real time. In recognition experiments, the optimised robust correlation outperformed two standard techniques based on the Dynamic Link Architecture [10].
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