On Using High-Definition Body Worn Cameras for Face Recognition from a Distance

Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range

A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose

Solving the Uncalibrated Photometric Stereo Problem using Total Variation

International audienceIn this paper we propose a new method to solve the problem of uncalibrated photometric stereo, making very weak assumptions on the properties of the scene to be reconstructed. Our goal is to solve the generalized bas-relief ambiguity (GBR) by performing a total variation regularization of both the estimated normal field and albedo. Unlike most of the previous attempts to solve this ambiguity, our approach does not rely on any prior information about the shape or the albedo, apart from its piecewise smoothness. We test our method on real images and obtain results comparable to the state-of-the-art algorithms

Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion

Abstract. The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a novel perspective revealing the two key factors in information utilization: class-relevance and redun-dancy. We derive a new information decomposition model where a novel concept called class-relevant redundancy is introduced. Subsequently a new algorithm called Conditional Informative Feature Extraction is for-mulated, which maximizes the joint class-relevant information by explic-itly reducing the class-relevant redundancies among features. To address the computational difficulties in information-based optimization, we in-corporate Parzen window estimation into the discrete approximation of the objective function and propose a Local Active Region method which substantially increases the optimization efficiency. To effectively utilize the extracted feature set, we propose a Bayesian MAP formulation for feature fusion, which unifies Laplacian Sparse Prior and Multivariate Logistic Regression to learn a fusion rule with good generalization ca-pability. Realizing the inefficiency caused by separate treatment of the extraction stage and the fusion stage, we further develop an improved design of the framework to coordinate the two stages by introducing a feedback from the fusion stage to the extraction stage, which signifi-cantly enhances the learning efficiency. The results of the comparative experiments show remarkable improvements achieved by our framework.

Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary

11th European Conference on Computer Vision, Heraklion, Crete, Greece, 5-11 Sep. 2010By coding the input testing image as a sparse linear combination of the training samples via l1-norm minimization, sparse representation based classification (SRC) has been recently successfully used for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to sparsely code the occluded portions in face images, SRC can lead to robust FR results against occlusion. However, the large amount of atoms in the occlusion dictionary makes the sparse coding computationally very expensive. In this paper, the image Gabor-features are used for SRC. The use of Gabor kernels makes the occlusion dictionary compressible, and a Gabor occlusion dictionary computing algorithm is then presented. The number of atoms is significantly reduced in the computed Gabor occlusion dictionary, which greatly reduces the computational cost in coding the occluded face images while improving greatly the SRC accuracy. Experiments on representative face databases with variations of lighting, expression, pose and occlusion demonstrated the effectiveness of the proposed Gabor-feature based SRC (GSRC) scheme.Department of ComputingRefereed conference pape

A Direct Method for Real-Time Tracking in 3-D under Variable Illumination

D tracking of free-moving objects has to deal with brightness variations pronounced by the shape of the tracked surface. Pixel-based tracking techniques, though versatile, are particularly a#ected by such variations. Here, we evaluate two illumination-adaptive methods for a novel e#cient pixel-based 3-D tracking approach. Brightness adaption by means of an illumination basis is compared to with a template update strategy with respect to both robustness and accuracy on tracking in 6 degrees-of-freedom