The 2011 International Joint Conference on Neural Networks最新文献_第10页

A novel facial feature extraction method based on ICM network for affective recognition 一种基于ICM网络的情感识别人脸特征提取方法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033469

F. Mokhayeri, M. Akbarzadeh-T.

{"title":"A novel facial feature extraction method based on ICM network for affective recognition","authors":"F. Mokhayeri, M. Akbarzadeh-T.","doi":"10.1109/IJCNN.2011.6033469","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033469","url":null,"abstract":"This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"34 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125615036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Self-Organizing Neural Population Coding for improving robotic visuomotor coordination 改进机器人视觉运动协调的自组织神经群编码

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033393

Tao Zhou, P. Dudek, Bertram E. Shi

引用次数: 7

Natural language generation using automatically constructed lexical resources 使用自动构建的词汇资源生成自然语言

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033329

Naho Ito, M. Hagiwara

引用次数: 9

A new algorithm for graph mining 一种新的图挖掘算法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033330

B. Chandra, Shalini Bhaskar

引用次数: 1

Adaptive self-protective motion based on reflex control 基于反射控制的自适应自保护运动

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033596

T. Shimizu, R. Saegusa, Shuhei Ikemoto, H. Ishiguro, G. Metta

引用次数: 3

Finding dependent and independent components from two related data sets 从两个相关的数据集中找到依赖的和独立的组件

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033257

J. Karhunen, T. Hao

{"title":"Finding dependent and independent components from two related data sets","authors":"J. Karhunen, T. Hao","doi":"10.1109/IJCNN.2011.6033257","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033257","url":null,"abstract":"Independent component analysis (ICA) and blind source separation (BSS) are usually applied to a single data set. Both these techniques are nowadays well understood, and several good methods based on somewhat varying assumptions on the data are available. In this paper, we consider an extension of ICA and BSS for separating mutually dependent and independent components from two different but related data sets. This problem is important in practice, because such data sets are common in real-world applications. We propose a new method which first uses canonical correlation analysis (CCA) for detecting subspaces of independent and dependent components. Standard ICA and BSS methods can after this be used for final separation of these components. The proposed method performs excellently for synthetic data sets for which the assumed data model holds exactly, and provides meaningful results for real-world robot grasping data. The method has a sound theoretical basis, and it is straightforward to implement and computationally not too demanding. Moreover, the proposed method has a very important by-product: its improves clearly the separation results provided by the FastICA and UniBSS methods that we have used in our experiments. Not only are the signal-to-noise ratios of the separated sources often clearly higher, but CCA preprocessing also helps FastICA to separate sources that it alone is not able to separate.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133211857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Conditional multi-output regression 条件多输出回归

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033220

Chao Yuan

引用次数: 2

Sparse analog associative memory via L1-regularization and thresholding 基于l1正则化和阈值的稀疏模拟联想记忆

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033470

R. Chalasani, J. Príncipe

引用次数: 0

Robust Jordan network for nonlinear time series prediction 非线性时间序列预测的鲁棒Jordan网络

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033550

Q. Song

引用次数: 4

Forecasting time series with a logarithmic model for the Polynomial Artificial Neural Networks 多项式人工神经网络的对数模型预测时间序列

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033576

J. C. Luna-Sanchez, E. Gómez-Ramírez, K. Najim, E. Ikonen

引用次数: 2