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Classification Enhancement via Biometric Pattern Perturbation

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Audio- and Video-Based Biometric Person Authentication (AVBPA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3546))

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Abstract

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.

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© 2005 Springer-Verlag Berlin Heidelberg

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Riopka, T., Boult, T. (2005). Classification Enhancement via Biometric Pattern Perturbation. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_89

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  • DOI: https://doi.org/10.1007/11527923_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27887-0

  • Online ISBN: 978-3-540-31638-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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