Praveen Sankaran

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A modified approach on modular PCA for face recognition is presented in this paper. The proposed changes aim to improve the recognition rates for modular PCA for face images with large variation in light and facial expression. The eyes form one of the most invariant regions on the face. A sub-image from this region is considered. Weight vectors from this(More)
A new wavelet-based image enhancement algorithm is proposed to improve performance of face detection in non-uniform lighting environment with high dynamic range. Wavelet transform is used for dimension reduction so that dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients. The normalized(More)
Image fusion is a technique of combining two or more images so that the combined image is better enhanced than all these images. We propose that a fusion based approach on Multi Scale Retinex with Color Restoration(MSRCR) would give better image enhancement. Lower dynamic range of a camera as compared to human visual system causes images taken to be(More)
Quad Tree algorithm is an efficient method for image compression at lower bit rates. It divides the image into four equal quadrants based on a threshold. The threshold is chosen from image characteristics using Otsu thresholding method. The image regions obtained from the Quad Tree decomposition are approximated by using a first order polynomial. Polynomial(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)
One of the most interesting problems in recent times in image processing and computer vision is fog, haze and rain removal from images. In this paper we shall consider the problem of haze removal. One of the latest haze removal algorithms proposed by Kaiming et al. [1] uses a dark channel prior based approach for haze removal. Though this approach gives(More)
Real-time tracking and recognizing multiple faces in complex environments has the ability to provide efficient security automation to large areas. Previous research has shown that Kalman filter techniques paired with the traditional face detection methods can be used to track one or more faces in a viewing region, but prove unreliable under variant(More)
Developing image enhancement algorithms require that there exists a proper evaluation scheme to compare different algorithms. Most objective algorithms fail because they are not designed for the subjective nature of the enhancement problem. This necessitates an evaluation scheme that would correlate with scores provided by a group of human observers. In(More)
Smooth varying data is hard to classify/divide to separate classes since there is small separation. Large number of close and adjacent poses create smooth varying manifolds. Manual class formation by selecting different data points from entire database into different training classes will affect the error rate in smooth varying data classification. This(More)
Atmospheric moisture, dust, smoke and vapor result in haze which tends to produce a distinctive gray or bluish hue and diminishes visibility. Acquired images can be used in applications such as surveillance, object identification, classification etc. only if the effect of weather is removed from them. One of the popular existing haze removal algorithms uses(More)