Image correction is discussed for realizing both effective object recognition and realistiic image-based rendering. Three image normalizations are compared in relation with the linear subspaces and eigenspaces, and we conclude that the normalization by LI-norm, which normalizes the total sum of intensities, is the best for our pwyoses. Based on noise analysis in the normalized image space(NIS),...

In previous work 6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image diier-ences, leading to a \dual" basis similar to eigenfaces 13]. The performance advantage of this probabilistic matching technique over standard E...

Facial recognition systems have their accuracy based on the intra-class variations between two stages in particular, enrollment and identification. These intra-class variations are affected by the lighting conditions with other reasons such as, facial expressions, pose, occlusion, poor sensor quality, and illumination quality. To identify a face or an object with accuracy, all such errors need ...

This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recently developed nonlinear techniques. The first nonlinear method, Locally Linear Embedding (LLE), maps the input data points to a single global coordinate system of lower dimension in a manner that preserves the relationships between neighboring points. The second method, Isomap, computes geodesic d...

Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter, we describe in rou...

In face recognition system, the efficiency and face detection rate is the key issue for designing the algorithm because the computer can recognize very large number of faces with high computational speed but it is not as efficient as human. There are many modern face recognition algorithms which efficiently detect face but also have false recognition. In this paper, we propose a new hybrid algo...

This report is divided into two parts. In the rst part we present an overview of sensorfusion. We analyze the single steps in a fusion process and describe in a systematic wayseveral methods for combining pattern classi ers. The second part describes some practicalexperiments with classi er combination in the eld of face recognition. We investigate theimpact of decision combinat...

In this paper we propose fast face recognition system based on the 1-D Discrete Slant Transform (ST) row feature vector-RV and column feature vector-CV. This scheme is less complicated and needs less time as compared to ST of full image. It is observed that in this method for 95% image energy the coefficient requirement reduces drastically compared to PCA and full ST. Thus computational burden ...

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE...

Human face is contexture multidimensional point of vision model and by creating computational model for human face recognition is too hard. The paper present two methodologies for the face recognition, the first one is feature extraction and second is the feed forward back propagation neural network. The feature extraction is with Principal Component Analysis and classification with the help of...