The Experts below are selected from a list of 8076 Experts worldwide ranked by ideXlab platform
Vijayan K Asari - One of the best experts on this subject based on the ideXlab platform.
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an improved face recognition technique based on modular pca approach
Pattern Recognition Letters, 2004Co-Authors: Rajkiran Gottumukkal, Vijayan K AsariAbstract:A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in Lighting Direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary even when the pose, Lighting Direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standard face databases.
Rajkiran Gottumukkal - One of the best experts on this subject based on the ideXlab platform.
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an improved face recognition technique based on modular pca approach
Pattern Recognition Letters, 2004Co-Authors: Rajkiran Gottumukkal, Vijayan K AsariAbstract:A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in Lighting Direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary even when the pose, Lighting Direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standard face databases.
John Porrill - One of the best experts on this subject based on the ideXlab platform.
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where is the light bayesian perceptual priors for Lighting Direction
Proceedings of The Royal Society B: Biological Sciences, 2009Co-Authors: James V Stone, I S Kerrigan, John PorrillAbstract:Perception of shaded three-dimensional figures is inherently ambiguous, but this ambiguity can be resolved if the brain assumes that figures are lit from a specific Direction. Under the Bayesian framework, the visual system assigns a weighting to each possible Direction, and these weightings define a prior probability distribution for light-source Direction. Here, we describe a non-parametric maximum-likelihood estimation method for finding the prior distribution for Lighting Direction. Our results suggest that each observer has a distinct prior distribution, with non-zero values in all Directions, but with a peak which indicates observers are biased to expect light to come from above left. The implications of these results for estimating general perceptual priors are discussed.
David J Kriegman - One of the best experts on this subject based on the ideXlab platform.
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eigenfaces vs fisherfaces recognition using class specific linear projection
European Conference on Computer Vision, 1996Co-Authors: Peter N Belhumeur, Joao P Hespanha, David J KriegmanAbstract:We develop a face recognition algorithm which is insensitive to gross variation in Lighting Direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination Direction lie in a 3-D linear subspace of the high dimensional feature space — if the face is a Lambertian surface without self-shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant and produces well separated classes in a low-dimensional subspace even under severe variation in Lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
Kavita Bala - One of the best experts on this subject based on the ideXlab platform.
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looking against the light how perception of translucency depends on Lighting Direction
Journal of Vision, 2014Co-Authors: Bei Xiao, Bruce Walter, Ioannis Gkioulekas, Todd Zickler, Edward H Adelson, Kavita BalaAbstract:Translucency is an important aspect of material appearance. To some extent, humans are able to estimate translucency in a consistent way across different shapes and Lighting conditions, i.e., to exhibit translucency constancy. However, Fleming and Bulthoff (2005) have shown that that there can be large failures of constancy, with Lighting Direction playing an important role. In this paper, we explore the interaction of shape, illumination, and degree of translucency constancy more deeply by including in our analysis the variations in translucent appearance that are induced by the shape of the scattering phase function. This is an aspect of translucency that has been largely neglected. We used appearance matching to measure how perceived translucency depends on both Lighting and phase function. The stimuli were rendered scenes that contained a figurine and the Lighting Direction was represented by spherical harmonic basis function. Observers adjusted the density of a figurine under one Lighting condition to match the material property of a target figurine under another Lighting condition. Across the trials, we varied both the Lighting Direction and the phase function of the target. The phase functions were sampled from a 2D space proposed by Gkioulekas et al. (2013) to span an important range of translucent appearance. We find the degree of translucency constancy depends strongly on the phase function's location in the same 2D space, suggesting that the space captures useful information about different types of translucency. We also find that the geometry of an object is important. We compare the case of a torus, which has a simple smooth shape, with that of the figurine, which has more complex geometric features. The complex shape shows a greater range of apparent translucencies and a higher degree of constancy failure. In summary, humans show significant failures of translucency constancy across changes in Lighting Direction, but the effect depends both on the shape complexity and the translucency phase function.