ABSTRACT Non-frontal illumination of objects may cause specular reflections and strong self-shado... more ABSTRACT Non-frontal illumination of objects may cause specular reflections and strong self-shadowing. Those phenomena change the appearance of objects to such an extent that they may not be recognized properly. We propose a method to automatically discard the areas of the image which are degraded beyond recovery by adverse illumination conditions. The method is based on a comparison between local variances of image gradient and is computationally efficient. We show that proposed method, implemented with a face verification system based on local DCTmod2 features and a GMM classifier, reduces total recognition errors in the presence of changing directional illumination conditions. Consequently, we show that proposed segmentation method can be used as an automatic estimator of mismatch between the illumination conditions present during the acquisition of training and testing images. We propose an adaptive thresholding scheme that uses the mismatch estimate to further reduce the recognition error.
The use of quality measures in biometrics is rapidly becoming the standard strategy for improving... more The use of quality measures in biometrics is rapidly becoming the standard strategy for improving performance of biometric systems, especially in the presence of variable environmental conditions of signal capture. It is often necessary to integrate multiple quality measures into the classification process in order to capture the relevant aspects of signal quality. The inclusion of multiple quality features quickly increases the dimensionality of the classification problem, which leads to the risks of overfitting and dimensionality curse. So ...
ABSTRACT Non-frontal illumination of objects may cause specular reflections and strong self-shado... more ABSTRACT Non-frontal illumination of objects may cause specular reflections and strong self-shadowing. Those phenomena change the appearance of objects to such an extent that they may not be recognized properly. We propose a method to automatically discard the areas of the image which are degraded beyond recovery by adverse illumination conditions. The method is based on a comparison between local variances of image gradient and is computationally efficient. We show that proposed method, implemented with a face verification system based on local DCTmod2 features and a GMM classifier, reduces total recognition errors in the presence of changing directional illumination conditions. Consequently, we show that proposed segmentation method can be used as an automatic estimator of mismatch between the illumination conditions present during the acquisition of training and testing images. We propose an adaptive thresholding scheme that uses the mismatch estimate to further reduce the recognition error.
The use of quality measures in biometrics is rapidly becoming the standard strategy for improving... more The use of quality measures in biometrics is rapidly becoming the standard strategy for improving performance of biometric systems, especially in the presence of variable environmental conditions of signal capture. It is often necessary to integrate multiple quality measures into the classification process in order to capture the relevant aspects of signal quality. The inclusion of multiple quality features quickly increases the dimensionality of the classification problem, which leads to the risks of overfitting and dimensionality curse. So ...
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