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Face detection and facial component extraction by wavelet decomposition and support vector machines
Dihua Xi,Seong-Whan Lee +1 more
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In this paper, a two-stage method using both SVM categories based on multiresolution wavelet decomposition (MWD) was presented, in which the whole face-based SVM was used for coarse location of faces from small sub-images of low resolution and then a set of component-based SVMs were applied to verify the extracted candidates in subsequent larger subimages of higher resolutions.Abstract:
Quite recently the support vector machine (SVM) has shown a great potential in the area of automatic face detection. Generally the SVM based methods fall into two categories: component-based and whole face-based. However there exist some limitations to each category. In this paper we present a two-stage method using both SVM categories based on multiresolution wavelet decomposition (MWD). In the first stage, the whole face-based SVMs are used for coarse location of faces from small sub-images of low resolution. Then a set of component-based SVMs are applied to verify the extracted candidates in subsequent larger sub-images of higher resolutions. Experimental results show that this wavelet-SVM based method takes the advantage of the effectiveness of both categories of SVM-based methods and the computation efficiency.read more
Citations
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Journal ArticleDOI
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
TL;DR: A novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns and a hand appearance-based sign verification method are included to further improve sign language spotting accuracy.
Proceedings ArticleDOI
Facial features extraction in color images using enhanced active shape model
TL;DR: An improved active shape model (ASM) for facial feature extraction that uses color information to improve the ASM approach and compensates for the effects of both head pose variations and the projection of 3D data to 2D.
Proceedings ArticleDOI
Human ear detection from side face range images
TL;DR: This paper introduces a simple and an effective method to detect ears, which has two stages: offline model template building and on-line detection.
Proceedings ArticleDOI
Using component features for face recognition
TL;DR: This work explores different strategies for classifier combination within the framework of component-based face recognition and proposes a novel Bayesian method which weighs the classifier outputs prior to their combination.
Journal ArticleDOI
Improved Active Shape Model for Facial Feature Extraction in Color Images
TL;DR: An improved Active Shape Model for facial features extraction relies on initializing the ASM model using the centers of the mouth and eyes using color information, incorporating RGB color information to represent the local structure of the feature points, and applying 2D affine transformation in aligning the facial features that are perturbed by head pose variations.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Proceedings ArticleDOI
Training support vector machines: an application to face detection
TL;DR: A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated.
Journal ArticleDOI
Face Detection
Erik Hjelmås,Boon Low +1 more
TL;DR: A comprehensive and critical survey of face detection algorithms, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods, is presented.