face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed. Index Terms—Facial image representation, local binary pattern, component-based face recognition, texture features

In this article, we review diverse studies of the antecedents, facial display, and social consequences of embarrassment. These studies indicate that embarrassment serves an appeasement function, reconciling social relations when they have gone awry. We then speculate about how embarrassment

in image coding, texture discrimination and fractal analysis. Index Terms-Coding, fractals, multiresolution pyramids, quadrature mirror filters, texture discrimination, wavelet transform. I I.

histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and im-age histograms and a

We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and non-rigid objects such as hands.

> ! "GZip" page 64, Table 2.5, line "progp": "43,379" ! "49,379" page 68, Table 2.6: "Mbyte/sec" ! "Mbyte/min" twice in the body of the table, and in the caption "Mbyte/second" ! "Mbyte/minute" page 70, para 4, line 5: "Santos" ! "Santis" page 71, line 11: "Fiala and Greene (1989)" ! "Fiala and Green (1989)" Chapter Three page 89, para starting "Using this method", line 2: "hapax legomena " ! "hapax legomenon " page 96, line 5: "a such a" ! "such a" page 98, line 6: "shows that in fact none is an answer to this query" ! "shows that only document 2 is an answer to this query" page 106, para 3, line 9: "the bitstring in Figure 3.7b" ! "the bitstring in Figure 3.7c" page 107, Figure 3.7: The coding shown in part (c) cannot be decoded ambiguously. For example, the sequence "1010 0000 0001 0000

other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] algorithm as being the most resistant to common image

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image

This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class