2013-07-26

一论文被《模式识别与人工智能》录用

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

摘要 考虑到不同部件(眼睛,嘴巴等)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上进行了人脸(表情)识别以及有遮挡的人脸(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很好的调节各部件的权重,优于各单一部件和简单的多部件融合方法的性能。

2013-07-22

IEEE SMC213 accepted papers are now available.

The accepted papers are now also available on the conference website:

 

http://www.smc2013.org/accepted_papers

 

Two paper have been accepted by IEEE SMC2013

Discriminant Multi-Component Face Analysis

Hongli Liu, Weifeng Liu*, Yanjiang Wang

Abstract—Sparse representation based classification (SRC) has attracted much attention in face analysis such as and . Currently, most of SRC based methods treated face as a whole component which results in under-utilization of the complementary in different facial parts. In this paper, we present an approach which can effectively explore the complementary of different facial parts to boost the performance of face analysis. In particular, we employ multi-view sparse coding techniques to learn the factorized representation of different facial components. Furthermore, we incorporate label information into the objective function to enforce the discriminability. To evaluate the performance, we conduct face analysis experiments including FR and FER on JAFFE database. Experimental results demonstrate that the proposed method can significantly boost the performance of face analysis.

Self-explanatory Convex Sparse Representation for Image classification

Baodi Liu, Yuxiong Wang, Bin Shen, Yujin Zhang, Yanjiang Wang, Weifeng Liu

Abstract-Sparse representation technique has been widely used in various areas of computer vision over the last decades. Unfortunately, in the current formulations, there are no explicit relationship between the learned dictionary and the original data. By tracing back and connecting sparse representation with the $K$-means algorithm, a novel variation scheme termed as self-explanatory convex sparse representation (SCSR) has been proposed in this paper. To be specific, the basis vectors of the dictionary are refined as convex combination of the data points. The atoms now would capture a notion of centroids similar to K-means, leading to enhanced interpretability. Sparse representation and K-means are thus unified under the same framework in this sense. Besides, an appealing property also emerges that the weight and code matrices both tend to be naturally sparse without additional constraints. Compared with the standard formulations, SCSR is easier to be extended into the kernel space. To solve the corresponding sparse coding subproblem and dictionary learning subproblem, block-wise coordinate descent and Lagrange multipliers are proposed accordingly. To validate the proposed algorithm, it is implemented in image classification, a successful applications of sparse representation. Experimental results on several benchmark data sets, such as UIUC-Sports, Scene 15, and Caltech-256 demonstrate the effectiveness of our proposed algorithm.