Satyanadh Gundimada

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A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images(More)
A rotation invariant human face detection system in color images based on human skin color distribution and intensity is proposed in this paper. Skin color distribution typical to a human face is used as a feature along with the intensity variations to classify the candidate regions into faces and nonfaces. The detection process is carried out in YCbCr(More)
A novel feature selection strategy to improve the recognition accuracy on the faces that are affected due to nonuniform illumination, partial occlusions and varying expressions is proposed in this paper. This technique is applicable especially in scenarios where the possibility of obtaining a reliable intra-class probability distribution is minimal due to(More)
FRGC aims to develop algorithms that make use of the high quality images in the face database. The algorithms in this paper have been developed with this in view. We present methods to estimate pose using multi-view classifiers. Based on the knowledge of pose and face geometry a region of interest of possible eye locations is found. An adaptive thresholding(More)
A rotation and scale invariant face detection algorithm based on the texture of a human face is proposed. Wavelet packet analysis is performed on the test image to get the coefficients. It is observed that wavelet packet decomposition until third level is sufficient enough to get the necessary frequencial components essential for classifying faces and(More)
An adaptive skin segmentation algorithm robust to illumination changes and skin like backgrounds is presented in this paper. Skin pixel classification has been limited to only individual color spaces. There has not been a comprehensive evaluation of which color components or a combination of color components would provide the best skin pixel classification.(More)