Detection of human faces from discriminative regions
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Detection of human faces from discriminative regions

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Published by Department of Electronic and Electrical Engineering, University of Surrey in Guildford .
Written in English

Book details:

Edition Notes


StatementP.Bílek ... [et al.].
SeriesVision, speech & signal processing : technical report -- VSSP-TR-2/2001, Vision, speech & signal processing -- VSSP-TR-2/2001.
ContributionsBílek, P., University of Surrey. Department of Electronic and Electrical Engineering.
ID Numbers
Open LibraryOL16366529M

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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We propose a robust method for face detection based on the assumption that face can be represented by arrangements of automatically detectable discriminative regions. The appearance of face is modeled statistically in terms of local photometric information and the spatial relationship of the discriminative regions. Generative and discriminative face modelling for detection. the 2d image appearance of human face. into a subspace in a manner which discounts those regions of the face with large. Despite superior performance of Local Binary Pattern (LBP) in texture classification and face detection, its performance in human detection has been limite Human detection using Discriminative and Robust Local Binary Pattern - IEEE Conference PublicationCited by: man faces, are one of most common and very speciflc objects, that we try to trace in images. The purpose of automatic in-image face detection methods is obvious: their primary goal is to segment image into regions that contain human face or its parts and into regions which can be - because they don’t represent nor human face neither any of its.

Detection of Human Faces under Scale, Orientation and Viewpoint Variations Kin Choong Yow and Roberto Cipolla Department of Engineering into face candidates (fig. 4). formed new region formed formed new region formed new region feature region feature pair partial face group face candidate Figure 4. Attentive feature grouping. Human Face Region Detection Driving Activity Recognition in Video: /ch Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for.   Face recognition via deep learning has achieved a series of breakthrough in these years [25, 27, 29, 30, 34, 37].The idea of mapping a pair of face images to a distance starts from [].They train siamese networks for driving the similarity metric to be small .   Three discriminative representations for face presentation attack detection are introduced in this paper. Firstly we design a descriptor called spatial pyramid coding micro-texture (SPMT) feature to characterize local appearance information. Secondly we utilize the SSD, which is a deep learning framework for detection, to excavate context cues and conduct end-to-end face .

Two kinds of discriminative representation combinations for face presentation attack detection. • Effective combination of binocular depth with local appearance for presentation attack detection. • Powerful presentation attack detection model by cascading deep network with micro-texture feature. Face detection is a computer technology that determines the location and size of human face in arbitrary (digital) image. The facial features are detected and any other objects like trees, buildings and bodies etc are ignored from the digital image. human-computer interaction. Unfortunately, developing a computational model of face detection and recognition is quite difficult because faces are complex, multidimensional and meaningful visual stimuli. Face detection is used in many places now a days especially the websites hosting images like picassa, photobucket and facebook. The discrimination power of various human facial features is studied and a new scheme for automatic face recognition (AFR) is proposed. The first part of the paper focuses on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain.