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IEEE international conference on Systems, Man, and Cybernetics (SMC)

作者： Agrawal, Urvashi Giripunje, Shubhangi Bajaj, Preeti GH Raisoni Coll Engn Dept Elect Nagpur Maharashtra India

ISBN: (纸本)9781479906529

Expression recognition or Emotional state recognition using holistic and feature information is the vital step in Driver Assistance System. Many researchers have work on Facial Gesture or Emotion recognition independently. The purpose of the present paper is to deal with Simultaneous Facial Gesture tracking and Emotion recognition with soft computing tool like Fuzzy rule based system (FBS). In Human Centered Transportation large number of road accidents took place due to drowsiness or bad mood of the driver. The system proposed in this paper take into account both the Facial Gesture tracking and Emotion recognition so that if there is any sign of less attentiveness of the driver or driver's fatigue the car will be switch to automatic mode. A novel fuzzy system is created, whose rules is being defined through analysis of Facial Gesture variations. The idea behind this paper is to detect Facial Gesture by detecting the motion of eyes & lips along with classification of different facial expressions into one of the four basic human emotions, viz. happy, anger, sad, and surprise with fuzzy rule based system for better system performance. The given system proposes 91.66% accuracy for Facial Gesture detection & 90% accuracy for Emotion recognition while using Simultaneous Facial Gesture detection and Emotion recognition it provides 94.58% accuracy.

关键词： HCTS Driver Assistance System (DAS) Facial Gesture Emotion recognition Fuzzy Rule Based System (FBS)

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Extracting human attributes using a convolutional neural network approach

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pattern recognition LETTERS 2015年 68卷 250-259页

作者： Perlin, Hugo Alberto Lopes, Heitor Silverio Parana Fed Inst Parana Paranagua PR Brazil Univ Tecnol Fed Parana Curitiba Parana Brazil

Extracting high level information from digital images and videos is a hard problem frequently faced by the computer vision and machine learning communities. Modern surveillance systems can monitor people, cars or objects by using computer vision methods. The objective of this work is to propose a method for identifying soft biometrics, in the form of clothing and gender, from images containing people, as a previous step for further identifying people themselves. We propose a solution to this classification problem using a Convolutional Neural Network, working as an all-in-one feature extractor and classifier. This method allows the development of a high-level end-to-end clothing/gender classifier. Experiments were done comparing the CNN with hand-designed classifiers. Also, two different operating modes of CNN are proposed and coin pared each other. The results obtained were very promising, showing that is possible to extract soft-biometrics attributes using an end-to-end CNN classifier. The proposed method achieved a good generalization capability, classifying the three different attributes with good accuracy. This suggests the possibility to search images using soft biometrics as search terms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Computer vision Machine learning soft-biometrics Convolutional Neural Network Gender recognition Clothes parsing

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A Study on Automatic recognition of Positive and Negative Emotions in Speech 18

A Study on Automatic Recognition of Positive and Negative Em...

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18th international conference on System Theory, Control and computing (ICSTCC)

作者： Pavaloi, I Ciobanu, A. Luca, M. Musca, E. Barbu, T. Ignat, Anca Romanian Acad IIT Inst Comp Sci Iasi Romania Alexandru Ioan Cuza Univ Fac Comp Sci FII Iasi Romania

ISBN: (纸本)9781479946013

The paper is focused on an experimental study on positive and negative emotion vocal recognition. After some considerations about the positive and negative emotions, the paper gives a short description of the three corpuses used in the work we have accomplished. The paper describes three sets of coefficients used, the statistic features used to generate the three sets of feature vectors and the two classification methods used in this study. The recognition results obtained for every corpus are shown and some conclusions and directions of development are presented.

关键词： pattern classification speech processing speech recognition k-NN SVM positive and negative emotion recognition

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Tree based Behavior monitoring for adaptive fraud detection

Tree based Behavior monitoring for adaptive fraud detection

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18th international conference on pattern recognition (ICPR 2006)

作者： Xu, Jianyun Sung, Andrew H. Liu, Qingzhong Microsoft Corp 1 Microsoft Way Redmond WA 98052 USA New Mexico Inst Min & Technol Dept Comp Sci Socorro NM 87801 USA

ISBN: (纸本)0769525210

The general basis for anomaly detection and fraud detection is pattern recognition. An effective online fraud detection system should be able to discover both known and new attacks as early as possible. The detection process should be self-adjustable to allow the system to deal with the constantly changing nature of online attacks. In this paper, we present an anomaly detection technique based on behavior mining and monitoring that work at both the individual and system level. Frequent pattern tree is utilized to profile the normal behavior adaptively. A novel tree-based pattern matching algorithm is designed to discover individual level anomalies. An algorithm for computing tree similarity is proposed to solve the system level problems. Empirical evaluations of our technique on both synthetic and real-world data show that we can accurately differentiate anomalous behaviors from the profiled normal behavior.

关键词： pattern recognition

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The study of soft sensor modeling method based on wavelet neural network for sewage treatment

The study of soft sensor modeling method based on wavelet ne...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Gao, Mei-Juan Tian, Jing-Wen Li, Kai Beijing Union Univ Dept Automat Control Beijing 100101 Peoples R China Beijing Univ Chem Technol Sch Informat Sci Beijing 100029 Peoples R China

ISBN: (纸本)9781424410651

Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameter of sewage treatment quality can not he detected on-line, a soft sensor Modeling method based on wavelet neural network is presented. The wavelet network structure for soft sensor of sewage treatment quality is established. We adopt a method of reduce the number of the Wavelet basic function by analysis the sparse property of sample data, the learning algorithm bayed on the gradient descent was used to train network. With the ability of strong function approach and fast convergence of wawelet network the soft sensor modeling method can truly detect and assess the quality of sewage treatment in real time by learning the sewage treatment parameter information of sensors acquired. The defection results show that this method is feasible and effective.

关键词： wavelet neural network sewage treatment soft sensor modeling

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Application of Common ANN for Similar Datatypes in On-line Monitoring and Security Estimation of Power System

Application of Common ANN for Similar Datatypes in On-line M...

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Springer international conference on Emerging Technologies in Data Mining and Information Security (IEMIS)

作者： Tiwary, Shubhranshu Kumar Pal, Jagadish Chanda, Chandan Kumar IIEST Dept EE Sibpur 711103 Howrah India

ISBN: (纸本)9789811319518;9789811319501

Monitoring a power network is an important task which is very complicated. To monitor the power systems, the developed nations are relying and shifting more and more towards soft-computing and pattern recognition techniques with the rapid improvements in the computation. In the work elaborated here, a report on the employment of a common multilayer feed-forward net, to the security estimation of a power network has been reported. The model built, is a 5-bus system, developed on the Simulink environment of a MATLAB R2013a version 32-bit software. The outcome was confirmed on a Hardware-In-Loop (HIL) device, on RT Lab Simulator of OPAL RT Technologies. The analysis is presented in this work for the perusal of the readers.

关键词： soft computing

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pattern Classification Using Class-Dependent Rough-Fuzzy Granular Space 1

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5th international conference on Rough Set and Knowledge Technology (RSKT)

作者： Pal, Sankar K. Meher, Saroj K. Dutta, Soumitra Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India INSEAD F-77305 Fontainebleau France

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

The article describes a new rough-fuzzy model for pattern classification. Here, class-dependent granules are formulated in fuzzy environment that preserve better class discriminatory information. Neighborhood rough sets (NRS) are used in the selection of a subset of granulated features that explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of class-dependent fuzzy granulation and NRS that is useful in pattern classification with overlapping classes. The superiority of the proposed model to other similar methods is demonstrated with both completely and partially labeled data sets using various performance measures. The proposed model learns well even with a lower percentage of training set that makes the system fast.

关键词： Fuzzy information granulation rough neighborhood sets rough-fuzzy granular computing pattern recognition soft computing

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Proceedings of the international conference on Artificial Intelligence

Proceedings of the International Conference on Artificial In...

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Proceedings of the international conference on Artificial Intelligence, IC-AI 2003

ISBN: (纸本)1932415122

This Volume 1 of the conference proceedings contains 76 papers. Topics discussed include intelligent pervasive computing, applications of artificial intelligence in finance and economics, algorithms. neural networks, soft computing for pattern recognition and intelligent control, constrain solving and programming, biometric identification and authentication, artificial intelligence approaches to bioinformatics, applications of advanced artificial intelligence techniques to solve company-related problems, fuzzy logic and fuzzy systems and genetic computing.

关键词： Artificial intelligence

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Detection of behavioural pattern through human-gait image using soft-computing technique

Detection of behavioural pattern through human-gait image us...

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international conference on Electrical, Electronics, Signals, Communication and Optimization, EESCO 2015

作者： Patra, Raj Kumar Sinha, Tilendra Shishir Dubey, Ravi Prakash Computer Science and Engineering Dr. C. V. Raman University Bilaspur Chhattisgarh State India ITMUR-School of Engineering and Research ITM University Raipur Chhattisgarh State India Institute of Engineering Dr. C. V. Raman University Bilaspur Chhattisgarh State India

ISBN: (纸本)9781479976768

In the recent times, it has been observed that most of the biometrical research work has been carried out either using template-based or knowledge-based approach. In the present paper, behavioural patterns have been determined through human-gait image through knowledge-based modelling using soft-computing technique. Very little amount of work has been carried out with knowledge-based or model-based approach using soft-computing tools like artificial neural network, fuzzy set theory and genetic algorithm. In the present work, soft-computing technique has been applied for the detection of behavioural patterns like: normal behaviour and abnormal behaviour. The work has been carried out in two phases. In the first phase, a knowledge-based model called artificial noise-free human-gait model (ANFHGM) has been formed. The model has been formed by considering an input human-gait image that has been enhanced and compressed for the removal of distortion with loss-less information. Later on it has been segmented using the mechanism for the detection of the region of interest along with object of interest. Hence the relevant geometric features have been extracted and stored in the form of a corpus. In the second phase, ANFHGM has been utilised for the determination of behavioural patterns with a test human-gait image. The process has been done by considering an unknown human-gait image for the detection of normal and abnormal behaviour patterns of the subject. For this an algorithm, known as BPDSC (Behavioural pattern detection using soft-computing) has been proposed in the present paper. The algorithm has been tested with 15 subjects of varying ages on a flat and plain surface. The result has been found very satisfactory with the tested data sets. © 2015 IEEE.

关键词： pattern recognition

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Michigan-style Fuzzy GBML with (1+1)-ES Generation Update and Multi-pattern Rule Generation 17

Michigan-style Fuzzy GBML with (1+1)-ES Generation Update an...

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Joint 17th World Congress of international-Fuzzy-Systems-Association / 9th international conference on soft computing and Intelligent Systems (IFSA-SCIS)

作者： Nojima, Yusuke Takemura, Shuji Watanabe, Kazuhiro Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781509049172

A variety of fuzzy genetics-based machine learning algorithms have been proposed in the frameworks of Michigan and Pittsburgh approaches. Since each individual is a single rule, Michigan-style algorithms need much less computation time than Pittsburgh-style algorithms where each individual is a rule set. For the same reason, Michigan-style algorithms cannot directly optimize rule sets. Rule set optimization is indirectly performed by optimizing each rule. In this paper, we propose the use of the (1+1)-ES generation update in Michigan-style algorithms. This is for directly performing rule set optimization without losing their high computational efficiency. We also propose a multi-pattern-based rule generation method to generate a fuzzy rule from multiple patterns in a heuristic manner. We demonstrate high efficiency and high generalization ability of our newly proposed Michigan-style algorithm through computational experiments on 19 data sets with 4-310 attributes and 2-15 classes.

关键词： Fuzzy genetics-based machine learning fuzzy classifier design (1+1)-ES generation update multi-pattern rule generation

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