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SVM based adaptive biometric image enhancement using quality assessment
Published in
Volume: 83
Pages: 351 - 371
The quality of input data has an important role in the performance of a biometric system. Images such as fingerprint and face captured under non-ideal conditions may require additional preprocessing. This chapter presents intelligent SVM techniques for quality assessment and enhancement. The proposed quality assessment algorithm associates the quantitative quality score of the image that has a specific type of irregularity such as noise, blur, and illumination. This enables the application of the most appropriate quality enhancement algorithm on the non-ideal image. We further propose a SVM quality enhancement algorithm which simultaneously applies selected enhancement algorithms to the original image and selects the best quality regions from the global enhanced image. These selected regions are used to generate single high quality image. The performance of the proposed algorithms is validated by considering face biometrics as the case study. Results show that the proposed algorithms improve the verification accuracy of face recognition by around 10-17%. © 2008 Springer-Verlag Berlin Heidelberg.
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JournalStudies in Computational Intelligence