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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory Faculty of Engineering The University of Tokushima Tokushima Japan

ISBN: (纸本)9781467369541

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification, we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for classification. In the feature extraction stage, we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face images. Then, for the purpose of solving the problem that features extracted by ICA are lack of classification information, we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary space. In the classification stage, the test samples are firstly projected to the independent subspace. After that, projected the samples which are difficult to identify to the LPE space and reclassified them. Finally, the results are been determined comprehensively. Experimental results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： Feature extraction Classification algorithms Face recognition Face Data mining Entropy Databases

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MINING THE IMPACT OF SOCIAL NEWS ON THE EMOTIONS OF USERS BASED ON DEEP MODEL

MINING THE IMPACT OF SOCIAL NEWS ON THE EMOTIONS OF USERS BA...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Fei Gao Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

In this paper,Deep Belief Nets(DBN) model and Support Vector Machine(SVM) are used to mine the features hidden in social news,which can influence the emotions of *** feature selection methods for text modeling are used to build the input vectors of DBN,with the purpose of keeping the text information to the greatest *** take advantage of the deep features abstracted by DBN to build social news text *** the same time,three optimal models are used as inputs of SVM to train and classify the social *** get a conclusion that DBN not only reduces the dimension of original features,but also makes the abstracted features with more text information and shows better performance in determining the influence on people' s emotions by social news.

关键词： Deep Belief Network(DBN) Restricted Boltzmann Machine(RBM) Impacts on Emotion Social News

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NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON MIXED MODEL

NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON ...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chongyuan Sun Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

The paper studies a new method for Chinese new word detection and emotional tendency judgment based on mixed model and proposes a new word generation *** we construct conditional random fields(CRFs) to recognize the new words,lead-in features based on character combined with the crowd sourcing network *** then express word as a word vector based on neural network language model(NNLM) to judge the new word emotional *** experimental results show that the method can improve the precision and recall of the new word detection with a good system performance,and it also provides a new way for forecasting the public mood.

关键词： New Word Detection Conditional Random Fields Emotional Tendency Judgment Neural Network Language Model

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Dynamic facial expression recognition based on K-order emotional intensity model

Dynamic facial expression recognition based on K-order emoti...

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IEEE International Conference on Robotics and Biomimetics

作者： Changqin Quan Yao Qian Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China P.R. The Japan Federation of Engineering Societies University of Tokushima Shinkura-cho Tokushima Japan

ISBN: (纸本)9781479973989

With the development of artificial intelligence and pattern recognition, facial expression recognition plays a more and more important role in intelligent human-computer interaction. In this paper, we present a model named K-order emotional intensity model (K-EIM) which is based on K-Means clustering. Different from other related works, the proposed approach can quantify emotional intensity in an unsupervised way. And then the output from K-EIM is encoded. The coding results are used for the dynamic facial expression recognition. The experiment is conducted on Cohn-Kanade facial expression database and the support vector machine classifier is used for facial expression classification. This method achieved a dynamic facial expression recognition accuracy of 88.32% which suggest that the proposed method shows better performance and proves its validity. Moreover, effect of different segments of emotional intensity is also discussed in the paper.

关键词： Face recognition Encoding Accuracy Emotion recognition Principal component analysis Training Feature extraction

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MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFICATION WITH FUZZY SVM

MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFI...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chengcheng Li Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

Micro-blog is easily posted and communicated because of its short text ***,its brevity limits the feature *** paper presents a blogger modeling method for Chinese micro-blog sentiment *** paper would adopt a multidimensional strategy to accomplish the feature extraction *** with traditional feature extraction methods and combined with the interactive property of blog,the blog comment will be considered as the performance characteristic of micro-blog emotion *** comes to comments content and the relationship between commentators and micro-bloggers while it extracts the micro blogging text *** last,the paper adopts fuzzy SVM model to reduce the sensation of noise points and outliers on classification *** results demonstrate the effectiveness of the multidimensional feature extraction method on sentiment classification and the classify effect of fuzzy SVM is better than SVM.

关键词： sentiment classification multidimensional fuzzy SVM

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FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MULTI-DECISION CLASSIFICATION

FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MUL...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology Faculty of Engineering The University of Tokushima770-8506TokushimaJapan

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification,we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for *** the feature extraction stage,we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face ***,for the purpose of solving the problem that features extracted by ICA are lack of classification information,we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary *** the classification stage,the test samples are firstly projected to the independent *** that,projected the samples which are difficult to identify to the LPE space and reclassified ***,the results are been determined *** results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： algorithms complementary space multi-decision classification face recognition

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Chinese Microblog Sentiment Classification Based on Deep Belief Nets with Extended Multi-Modality Features

Chinese Microblog Sentiment Classification Based on Deep Bel...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Xiao Sun Chengcheng Li Wanyi Xu Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China School of Information Science and Engineering Southeast University Shanghai China Faculty of Engineering University of Tokushima Tokushima Japan

ISBN: (纸本)9781479942732

This paper presents a DBN (deep belief nets) model and a multi-modality feature extraction method to extend features' dimensionalities of short text for Chinese micro blogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the micro blogging features according to the relationship between commenters and posters though constructing micro blogging social network as input information. Then, the integration of the above modality features is combined and represented as input vector for DBN. In this paper, a DBN model, which is stacked with several layers of RBM (Restricted Boltzmann Machine), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of original data to learn hidden structures for better feature representation. A Class RBM (Classification RBM) layer, which is stacked on top of several RBM layers, is adapted to achieve the final sentiment classification. The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than state of the art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.

关键词： Feature extraction Semantics Visualization Training Social network services Blogs Educational institutions

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Finding Emotional Focus for Emotion Recognition at Sentence Level

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电子学报(英文版) 2013年第1期22卷 99-103页

作者： QUAN Changqin REN Fuji Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei 230009 China Faculty of Engineering The University of Tokushima Tokushima Japan

Emotion recognition at sentence level is one of the fundamental problems of textual emotion un- derstanding. Based on the observation that sentence emo- tional focus can be expressed by some clauses in this sen- tence, this paper proposes to find the emotional focus for sentence emotion recognition. For the sake of breaking through the problems brought about by depending on emo- tion lexicons, we first recognize word emotions in a sen- tence based on Maximum entropy model. And then homo- geneous Markov model is built for clause emotion recogni- tion; After that, a strategy based on emotion selection is proposed for a sentence with multiple clauses, and genetic algorithm is used for clause selection by textual feature weighting. The experimental results show that, comparing with the baseline, there are 9.1% and 3.6% improvement respectively for two different evaluations. It is demon- strated that finding emotional focus by clause selection is able to improve the performance of sentence emotion recognition significantly.

关键词：情感识别句子层面聚焦马尔可夫模型最大熵模型特征加权遗传算法

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On the α-universal multiple I restriction method for general fuzzy reasoning

On the α-universal multiple I restriction method for genera...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Yiming Tang Xiaomei Li Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei P. R. China School of Computer and Information Hefei University of Technology Hefei P. R. China

Based on the differently implicational idea, the a-universal multiple I restriction method is put forward for general fuzzy reasoning, which contains the a-multiple I restriction method as its specific case. First of all, we give the a-universal multiple I restriction principle, which improves the previous restriction principle, and then provide the existing condition of the solutions of the new method. Furthermore, we obtain the optimal solution of the new method for the fuzzy implications with residual pair, the R-implications, as well as some particular fuzzy implications. Finally, it is found that the new method is more reasonable by contrast with the a-multiple I restriction method.

关键词： Fuzzy reasoning Fuzzy systems Educational institutions Bismuth Conferences Abstracts Laboratories

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Speech emotion recognition using a novel feature set

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Journal of Computational Information Systems 2013年第15期9卷 6097-6104页

作者： Wang, Kunxia Li, Lian Yang, Jing Ren, Fuji Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei 230000 China School of Electricity and Information Engineering Anhui University of Architecture Hefei 230000 China

Speech emotion recognition has attracted attentions from increased number of researchers in Psychology, Computer science, Phonetics and related disciplines. This paper discusses combining multiple features for speech emotion recognition to yield improved performance. It proposes using combination of multiple features to improve recognition performance. Using ChineseLDC emotional database, the experiment results show that a novel feature set of MFCC, fundamental frequency, Δ MFCC, Δ Δ MFCC, energy, zero-crossing ratio and amplitude can get the best performance as 95% and 84% for emotional speech recognition of four emotional state and six emotional state. © 2013 Binary Information Press.

关键词： Speech recognition

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