2013-12-17

论文“基于多分离部件稀疏编码的人脸图像分析”正式出版

基于多分离部件稀疏编码的人脸图像分析
刘伟锋,刘红丽,王延江
中国石油大学信息与控制工程学院青岛266580

摘要 考虑到不同部件(眼睛,嘴等)对人脸分析的贡献差别,提出基于多部件稀疏编码的人脸图像分析方法.首先,选取对人脸(表情)分析影响较大的几个人脸部件,然后,利用多视角稀疏编码方法学习各部件的字典,并计算相应的稀疏编码,最后,将稀疏编码输入分类器(支持向量机和最小均方误差)进行判决.分别在数据库JAFFE和Yale上进行人脸(表情)识别及有遮挡的人脸(表情)识别实验.实验结果表明,基于多部件稀疏编码的人脸分析能较好地调节各部件的权重,优于各单一部件和简单的多部件融合方法的性能.

关键词 : 人脸部件,  人脸分析,  稀疏编码,  人脸识别,  表情识别

Abstract:Considering the different contributions of different facial components to face analysis,e.g. eyes,mouth etc.,a face analysis based on multi-component sparse coding is proposed. Firstly,some facial components which play important role to face analysis are selected. Then,the dictionaries of multiple components are learnt by using multi-view sparse coding algorithm,and the sparse codes of each face image are computed based on the dictionary. The final decision is made through pooling the sparse codes into support vector machines and least squares classifiers. Face analysis experiments include face recognition,facial expression recognition,face recognition with occlusion,and facial expression recognition with occlusion. The experimental results show that the proposed method based on multi-component sparse coding learns optimal weights of different facial components and outperforms single facial component method and simple multi-component fusion method.

Key words: Face Component     Face Analysis     Sparse Coding     Face Recognition    Facial Expression Recognition

http://118.145.16.223:81/Jweb_prai/CN/abstract/abstract9702.shtml

论文“混合表情的稀疏表达分析”正式出版

宋彩风,刘伟锋,王延江. 混合表情的稀疏表达分析[J]. 计算机工程与应用, 2013, 49(23): 122-126.
摘要 提出了一种混合表情的定量描述方法。基于压缩感知的理论框架,以面部特征点的Gabor小波系数为表情特征对混合表情进行了分析;利用隶属度函数定量表示混合表情中的不同组成。实验结果表明,该方法可以简单有效地表示混合表情中各基本表情的组成。
关键词: 表情分析   压缩感知   混合表情   Gabor   隶属度   截集

CVIU paper is online.

Please cite this article as: W. Liu, D. Tao, J. Cheng,  Y. Tang, Multiview Hessian Discriminative Sparse Coding for
Image Annotation, Computer Vision and Image Understanding, 118:50-60, (2013), doi:  http://dx.doi.org/10.1016/j.cviu.2013.03.007

Abstract

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation.

Keywords: Image annotation; Hessian; multiview; sparse coding