Incorporating Image Quality in Multimodal Biometric Verification
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Face Quality Assessment System in Video Sequences
Biometrics and Identity Management
High Frequency Assessment from Multiresolution Analysis
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Asymmetry-Based Quality Assessment of Face Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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A method using local features to assess the quality of an image, with demonstration in biometrics, is proposed. Recently, image quality awareness has been found to increase recognition rates and to support decisions in multimodal authentication systems significantly. Nevertheless, automatic quality assessment is still an open issue, especially with regard to general tasks. Indicators of perceptual quality like noise, lack of structure, blur, etc. can be retrieved from the orientation tensor of an image, but there are few studies reporting on this. Here we study the orientation tensor with a set of symmetry descriptors, which can be varied according to the application. Allowed classes of local shapes are generically provided by the user but no training or explicit reference information is required. Experimental results are given for fingerprint. Furthermore, we indicate the applicability of the proposed method to face images.