Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
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This paper presents a novel technique for improving face recognition performance by predicting system failure, and, if necessary, perturbing eye coordinate inputs and repredicting failure as a means of selecting the optimal perturbation for correct classification. This relies on a method that can accurately identify patterns that can lead to more accurate classification, without modifying the classification algorithm itself. To this end, a neural network is used to learn 'good' and 'bad' wavelet transforms of similarity score distributions from an analysis of the gallery. In production, face images with a high likelihood of having been incorrectly matched are reprocessed using perturbed eye coordinate inputs, and the best results used to “correct” the initial results. The overall approach suggest a more general approach involving the use of input perturbations for increasing classifier performance in general. Results for both commercial and research face-based biometrics are presented using both simulated and real data. The statistically significant results show the strong potential for this to improve system performance, especially with uncooperative subjects.