InTechOpen uses cookies to offer you the best online experience. By continuing to use our site, you agree to our Privacy Policy.

Computer and Information Science » Human-Computer Interaction » "State of the art in Biometrics", book edited by Jucheng Yang and Loris Nanni, ISBN 978-953-307-489-4, Published: July 27, 2011 under CC BY-NC-SA 3.0 license. © The Author(s).

Chapter 12

Bi-Modality Anxiety Emotion Recognition with PSO-CSVM

By Ruihu Wang and Bin Fang
DOI: 10.5772/18245

Article top


Anxiety or stress emotion representation
Figure 1. Anxiety or stress emotion representation
System Framework Schematic of Human Motion Recognition
Figure 2. System Framework Schematic of Human Motion Recognition
Bi-Modality Fusion Affective recognition
Figure 3. Bi-Modality Fusion Affective recognition
Serial cascade SVMs for gene expression data classification
Figure 4. Serial cascade SVMs for gene expression data classification
PSO-SVM based feature selection and parameter optimization architechture scheme
Figure 5. PSO-SVM based feature selection and parameter optimization architechture scheme
Algorithm for SVM Training and Classification with PSO
Table 2. Algorithm for SVM Training and Classification with PSO
Cascaded Architecture of Serial SVMs
Figure 6. Cascaded Architecture of Serial SVMs
PSO-Cascaded SVMs for Feature Selection and Parameters Optimization
Figure 7. PSO-Cascaded SVMs for Feature Selection and Parameters Optimization

Bi-Modality Anxiety Emotion Recognition with PSO-CSVM

Ruihu Wang1, 2 and Bin Fang2

1. Introduction

In Human Computer Interaction (HCI) community, the ultimate goal which we are striving to achieve is to create a natural, harmony way of bi-directional communication between machine and human. As we well known, many machine intelligence applications are based on machine vision technology, for instance, industrial detection, face recognition, medical image computer aided diagnose (MICAD), fingerprint recognition, and so on. In terms of artificial intelligence, it is built upon digital image processing and video frame analyzing to extract feature information to recognize some interesting objects. Moreover, a high-level machine learning system should be capable of identifying human emotion states and make interactions accordingly. Multimodal human emotion recognition involving facial expression and motion recognition could be applied to intelligent video surveillance system to provide an early warning mechanism in case of potential unsafe action occurring. There is no doubt that the ever increasing various kinds of crimes in our modern society which make the living environment around us even worse, demand for an intelligent and automatic security precautions measures to offer people a more convenient, relaxed living conditions. Meanwhile, automatic intelligent video-based surveillance system has received a lot of interest in the computer vision and human computer interaction community in recent years. CMU’s Video Surveillance and Monitoring (VSAM) project [26] and MIT AI Lab’s Forest of Sensors project [27] are examples of recent research efforts in this field. As a matter of fact, the safeguard has to keep watch on lots of screens in control center in real application. The video displayed on the screens are captured from cameras which are distributed in various security-sensitive areas such as elevator, airports, railway station or public places. However, because of human’s inherent information processing limitation, it is impossible to pick up all of the useful information from the monitors at the same time properly and spontaneously. That means some potentially dangerous information could be missed out. We expect the human computer interaction system is able to take the information-processing burden off the human. An ideal and effective intelligent surveillance system should work automatically without or with minimal human intervention. In addition, we believe that an intelligent surveillance system should be capable of preventing and predict criminal occurring by biometric recognition, rather than identifying the suspect after attacks happened. Figure 1 shows some pictures in which the subjects are under the anxiety or stress emotion state when she interviewed with human resource manager, not criminal nevertheless.


Figure 1.

Anxiety or stress emotion representation

A number of universities and research groups, commercial companies are conducting meaningful projects and have made significant progress in visual surveillance monitoring system. The Safehouse Technology Pty Ltd in Australia developed The Clarity Visual Surveillance System (CVSS) [28], which provides security professionals with the next generation in visual surveillance management. Although state-of-the-art computer vision technology and artificial intelligence algorithm were used, the system did not integrate affective computing methods in it yet. Picard defined “Affective Computing” as: computing that relates to, arises from, or deliberately influences emotions. Affective computing expands human-computer interaction by including emotional communication together with appropriate means of processing affective information [29]. In affective computing research, a variety of input signals like visual, verbal, touch/wearable computing, etc, have been used into emotion communication frequently in most cases. However, visual channel signal, which involves facial expression and gesture motion would be considered only in our surveillance system. It is a typical non-intrusive, intelligent analyzing technology and easily to be implemented. It does not require the subject to comply with some requirement he/she probably won’t to meet, for example, iris recognition, fingerprint recognition and wearable device. In a survey of hundreds of US security executives, Buxton [1] pointed out that system which could process the video from the increasing number of cameras were “one of the top items in demand”. We believe that visual surveillance based on emotion perception and recognition is the next generation of intelligent monitoring system. More importantly, this system can be capable of supervising individuals in specific environment to carry out data analysis and recommend possible interventions.

2. Previous work

Psychological research findings suggest that humans make judgment action depend on the combined visual modalities of face and body more effective than any other channel [18]. Since Paul Ekman and Frisen [3] divided human emotion into six primary categories: happiness, sadness, disgust, angry, surprise, fear, there has been a great amount of research on facial expression recognition. Eckman revealed that the facial expression showed the inside emotional state and the specific sense at that time, which was hardly controlled by human artificially. In automatic facial expression recognition, feature extraction and classifier design are two major procedures. As shown in Table 1, the most frequently being used feature extraction method is Gabor wavelet feature. A subset of these filters is chosen using AdaBoost, which is transmitted for training SVM as a reduced feature representation. A wide range of surveillance systems have been developed for different applications. G. Lu, X. Li, H. Li proposed a surveillance system for nurses to distinguish the neonatal pain expression from non-pain expression automatically using Gabor wavelet transform and support vector machine. They reported 85.29% recognition rate of pain versus non-pain [2]. The Safe-house Technology Pty Ltd in Australia developed Clarity Visual Surveillance System. It employs state-of-the-art computer vision algorithm and artificial intelligence, whereas it did not involve affective computing technology and emotion recognition in it yet. Bouchrika introduced gait analysis for visual surveillance considering that the identification of the individuals who are suspected of committing crimes is more important than predict when a crime is about to happen [15]. P. Rani, N.Sarkar and J.Adams built an anxiety-recognition system capable of interpreting the information contained in physiological signals processes to predict the probable anxiety state [17]. In [18] H.Kage introduced pattern recognition technologies for video surveillance and physical security. Their system detects the occurrence of a relevant action via image motion analysis. Optical-flow is used to analyze motion and AdaBoost is been for face detection. In [18], H.Gunes and M.Piccardi presented an approach to automatic visual recognition of expressive face and upper-body gestures from video sequences which are suitable for human communicative behavior. They defined some rules for facial expression and body motion recognition respectively to make right decision firstly. Min-max analysis method is used to detect the eyebrows, eyes, mouth and chin by evaluating the topographic gray level relief. Body feature is extracted by using traditional image processing methods such as background subtraction and dilation.

In order to improve the accuracy of classifiers and provide fast, pertinent response, many researchers proposed optimized approaches. Typically Genetic Algorithm or Particle Swarm Optimization is often used for feature selection and parameters optimization [19,20,21,22]. In our work, the key problems are facial expression, head motion and hand gesture analysis. We also care more about run-time consumption which impacts the recognition efficiency.

Feature Extraction MethodsClassifier designOptimized Feature
Gabor wavelet[2,4,5,6,10,16] Optical Flow[7], PCA[12] FLD, Eigenfaces[9],
Hide Markov Model [11]
Support Vector Machine[2,6,7,8],
Artificial Neural Network[4,5],
Bayesian Network[6,16],

Table 1.

Overview of methods used in facial expression recognition

3. Methods

3.1. System framework

A visual automated surveillance system consists of three main phases: face and body motion detection, facial feature and motion feature extraction, classification and recognition, as shown in Figure 2. By means of sensitive facial expression extraction, the subject’s intention and prospective behaviour are analyzed to report any suspicious expression or activities to the control central. The system would be able to reduce potential crimes by recognizing suspicious individuals’ emotion state ahead of security threats happening.

The front end inputs involve three different parts: (1)static facial image database input; (2)static face image database input, and (3) real face image detection captured by cameras using image sequence analysis. All of these three channels have to be passed through the facial expression feature extraction procedure. AdaBoost, Optical Flow and Gabor feature can be used to extract facial expression feature. In the middle of the system framework, both automatic facial expression recognition and human motion recognition are implemented through classifier learning and test, which involve PSO-SVMs feature extraction and parameters optimization. In our system, we use cascaded SVM architecture combined with particle swarm optimization algorithm to train and test SVM to get an optimized classifier which runs faster in computational-consuming conditions.


Figure 2.

System Framework Schematic of Human Motion Recognition


Figure 3.

Bi-Modality Fusion Affective recognition

The integration of Bi-Modality feature fusion classification for affective state recognition is composed by two uni-modality signals, as shown in Fig 3. One is facial expression feature classification and fusion, the other is motion feature classification and fusion. The essential aspect for computer to understand human expression is to achieve head’s rigid motion and face’s non rigid motion by which machine can track and analyze the facial expression change. Among this, the head’s rigid motion can be denoted by six parameters: three rotation R(rx, ry, rz) parameters: head shifting around x, y and z axis, and three translation τ(tx,ty,tz) parameters: downward and upward, left and right, front and backward. Non rigid motion of Face includes movement of mouse, eye, eyebrow and winkle of forehead.

3.2. Cascaded SVM architecture

Support vector machine was developed by Vapnik from the theory of Structural Risk Minimization. However, the classification performance of the practically implemented is often far from the theoretically expected. In order to improve the the classification performance of the real SVM, some researchers attempt to employ ensemble methods, such as conventional Bagging and AdaBoost [29]. However, in [30,31], AdaBoost algorithm are not always expected to improve the performance of SVMs, and even they worsen the performance particularly. This fact is SVM is essentially a stable and strong classifier.

Considering the problem of classifying a set of training vectors belonging to two separate classes,


SVM can be trained by solving the following optimization problem:


subject to


where ξi>0 is the i-th slack variable and C is the regularization parameter.

Nonlinear SVM are known to lead to excellent classification accuracies in a wide-range of tasks. It utilizes a set of support vectors to define a boundary between classes, which is dependent on a kernel function. However, as a classifier, SVM is usually slower than neural networks. The reason for this is that the run-time complexity is proportional to the number of support vectors, i.e. to the number of training examples that the SVM algorithm utilizes in the expansion of the decision function [23].

The optimization problem of Nonlinear SVM with kernel function is denoted by


subject to


If α*=(α1*,,αl*)T is a solution, then

And (ω*,b*) is the optimized solution of (2.3)~(2.4). where K(xixj) is the kernel function performing the non-linear mapping into feature space. One of the most common used kernel function is Gaussian Radius Basis Function which is with the form,

According to Yang and Honvar [32], the choice of feature used to represent patterns that are presented to a classifier has great impact on several pattern recognition properties, including the accuracy of the learned classification algorithm, the time need for learning a classification function, and the number of examples needed for learning, the cost associated with the features. In addition to feature selection, C.Huang and C. Wang [33] suggested that proper parameters setting can also improve the SVM classification accuracy. The parameters include penalty parameter C and the kernel function parameter σfor RBF, which should be optimized before training. D. Iakovidis proposed a novel intelligent system for the classification of multiclass gene expression data. It is based on a cascading support vector machines scheme and utilize Welch’s t-test for the detection of differentially expressed genes. In their system, a 5-block cascading SVMs architecture was used for the 6-class classification problem as shown in Figure 4 [43]. The classification performance was evaluated by adopting a Leave-One-Out (LOO) cross validation approach. LOO is commonly used when the available dataset is small providing an almost unbiased estimate of the generalization ability of a classifier [44].


Figure 4.

Serial cascade SVMs for gene expression data classification

In order to achieve optimal feature subset selection and SVM-RBF parameters, Hsu and Lin [34] proposed a Grid algorithm to find the best C and sigma for RBF kernel. However their method has expensive computational complexity and does not perform well. Genetic algorithm is an another alternative tool, which has the potential to generate both the optimal feature subset and SVM parameters at the same time. Huang and Wang [33] conducted some experiments on UCI database using GA-based approach. Their result has better accuracy performance with fewer features than grid algorithm. Compared to Genetic Algorithm, Particle Swarm Optimization has no evolution operators such as crossover and mutation. There are few parameters to adjust. It works well in a wide variety of applications with slight variations [35].

In this paper, we proposed a cascaded SVM structure, as shown in Fig 6 to speed up body gesture classifier performance over conventional SVM-based methods without reducing detection rate too much and the hierarchical architecture of the detector also reduces the complexity of training of the nonlinear SVM classifier.

3.3. Standard particle swarm optimization algorithm

Particle Swarm Optimization was introduced firstly by James Kennedy and Russel Eberhart [36]. It is a population-based evolutionary computation search technique. In PSO, each potential solution is assigned a randomized velocity, and the potential solutions, called particles, fly through the problem space by following the current optimum particle. Each particle keeps track of its coordinates in hyperspace which are associated with the best solution (fitness) it has achieved so far. This value is called pbest. Another “best” value is also tracked. The “global” version of the PSO keeps track of the overall best value, and its location, which is called gbest [37]. Each particle is treated as a point in a D-dimension space. The original PSO algorithm is described below:


The ith particle’s location vector is represented aszi=(zi1,zi1,ziD); the velocity is denoted byvi=(vi1,vi1,viD). pi=(pi1,pi1,piD)and pg=(pg1,pg1,pgD) are pbest and gbest respectively. r1and r2 are two random numbers in the range [0,1].

Equation (8) describes the flying trajectory of a population of particles, how the velocity and the location of a particle is dynamically updated. Equation (8.1) consists of three parts. The first part is the momentum part. The velocity is changed by current value. The second part is the cognitive part which represents the particle’s learning capability from its own experience. The third part is the social part which represents the collaboration among all particles [38].

In order to improve the convergence performance of PSO algorithm, Shi and Eberhart proposed a modified particle swarm optimizer. An inertial weight w is brought into the original PSO algorithm. This w plays the role of balancing the global search and local search. It can be a positive constant or even a positive linear or nonlinear function of time [39].


Simulations have been conducted on this modified PSO to illustrate the impact of this parameter introduced. It was concluded that the PSO with the inertial weight in the range [0.9,1.2] on average will have a better performance. A time decreasing inertial weight can also bring in a significant improvement on the PSO performance, as shown in Equation (10):


In this paper, PSO is utilized to search the optimal solution of a RBFN-SVM by minimizing the cost function Φ as the fitness function. Each particle is encoded as a real string representing the kernel centers (z) and widths (σ) as well as linear model coefficients w and b. With the movement of the particles in the solution space, the optimal solution with a minimum value of the cost function Φ will be obtained. Optimizing the kernel centers and widths and the weights of the SVM model relating the feature variables synergistically keeps the model from getting trapped into a local optima and improves the model performance [40]. There are two key factors to determine the optimized hyperparameters when using PSO. One is how to represent the hyper-parameter as the particle’s position, namely how to encode. The other problem is how to define the fitness value function which evaluate the goodness of a particle.

In this section, we describe the proposed SVM system for classification task. The aim of this system is to optimize the SVM classifier accuracy by detecting the subset of the best discriminative feature and solving the SVM model selection.


Figure 5.

PSO-SVM based feature selection and parameter optimization architechture scheme

3.3.1. PSO setup

The position pid+2 of each particle Pi from the swarm is regarded as a vector encoding.

  1. Feature subset f selection: f is a candidate subset of features, and F is a features set which consists of d available input features.


  1. KBNF based SVM parameters optimization, includes C andσ.

The position vector of each particle can be represented as the other kind of form:


Let f(i) be the fitness function of the ith particlePi. As suggested by Melgani, the choice of the fitness function is important on which PSO evaluates each candidate solution pi based for designing SVM classifier. They explored a simple SV count as a fitness criterion in the PSO optimization framework.

3.3.2. SVM training and classification with PSO

The pseudo code of the proposed method for SVM-PSO classification is given below.


Table 2.

Algorithm for SVM Training and Classification with PSO

Many researchers have proposed cascaded SVMs architecture to solve the computational complexity problem. Among the cascaded structure, there are mainly two kinds of SVMs architecture. One is parallel SVMs, and the other is serial SVMs. The parallel cascaded structure of SVM is more relied on hardware architecture of computer, which is developed based on decomposing the original complex problem into a number of independent simplified smaller problems, and in the end the partial results are combined into an ultimate output [41]. Compared to parallel SVMs, the serial structure cascaded SVMs is more feasible and easily to be implemented. Y. Ma and X. Ding proposed a cost-sensitive SVM classifier to detect face [42]. In many classification cases, the cost of False Negative is far more than False Positive. In their cost-sensitive SVMs, different cost are assigned to two types of misclassification to train the cascaded SVMs in different stage of the face detector. The optimization goal is given by


where Cp is the cost for face samples, and Cn is for non-face samples, usuallyCp>Cn.


Figure 6.

Cascaded Architecture of Serial SVMs

We propose here a novel approach named PSO-Cascaded SVMs classification method to combine Cascaded SVMs with particle swarm optimization algorithm. As shown in Figure 6, there are some linear SVMs cascaded to form a serial structure at the front end and a nonlinear SVM at the end of this system.

We think of two optional strategies to introduce particle swarm optimization into the cascaded architecture of serial SVMs: one is integrate PSO into the serial linear SVMs and the other is integrate PSO into the end nonlinear SVM only. Regarding that the serial SVMs structure at the front end is more easily to construct and implement than nonlinear SVM, we take the later strategy. The nonlinear SVM classification is more complicated because it has to deal with feature subset selection and kernel function parameters optimization.


Figure 7.

PSO-Cascaded SVMs for Feature Selection and Parameters Optimization

In our experiment, only lip, eye and forehead changes are detected and analyzed. There are 4 states for lip, 3 states for eye and 3 states for forehead individually. We use 10-dimension feature vector to express 4*3*3 (36) kinds of states altogether, which means only 36 facial expressions are considered in our experiment, as shown in Table 3.

Facial RegionsState CodeState Description
Positive ClassNegative Class
I.4Bend up at the corner of the mouthBend up at the corner of the mouth
eyeII.1Eyelid OpenEyelid Mouth
II.2Eyeball turn leftEyeball turn left
II.3Eyeball overseeEyeball look up
foreheadIII.1Eyebrow raiserEyebrow lower
III.2Eyebrow tighterEyebrow stretcher
III.3Tipofbrow raiser,
Eyeborws lower
Tipofbrow lower,
Eyeborws raiser

Table 3.

Overview of methods used in facial expression recognition

The overall accuracy rate for facial expression recognition with our proposed method can achieve 83.5% under uniform illumination. The performance of PSO-SVM classifier learning and testing for features selecting and parameters optimization outweighed other methods like PCA-SVM, PCA-RBF, whose overall accuracy rate achieved 91.46%, whereas PCA-SVM was 85.54% and PCA-RBF 83.27%,respectively.

4. Conclusion and future work

In this paper we have proposed a fusion method for facial expression and gesture recognition to build a surveillance system. Different from traditional six basic emotions on which many researchers have worked, we care only about the anxiety emotion. However, facial expression information is not enough for computer to recognize, we syncretize body gesture recognition to obtain a combined approach. Among many kinds of classifiers, SVM shows good performance for small samples classification. Meanwhile SVM is computational time expensive tool, we have to deal with some optimization such as feature subset selection before training and classifying to improve its performance. Especially for nonlinear SVM, the selection of kernel function and parameters optimization is more important. The cascaded SVMs structure show up superior performance in pattern classification.

Further more, as a simple stochastic global optimization technique inspired by social behavior of bird flocking, PSO can be used into the cascaded SVMs to select feature subset and optimize parameter for kernel function. The performance PSO-SVM strategies have been proposed in this paper for SVM to enhance its learning and classifying capability.


This work is sponsored by the Science and Technology Foundations of Chongqing Municipal Education Commission under Grant No. KJ091216, and Excellent Science and Technology Program for Overseas Studying Talents of Chongqing Municipal Human Resources and Social Security Bureau under Grant No. 09958023, and also by Key project of Science and Research Foundation of Chongqing University of Arts and Sciences under Grant No. Z2009JS07.


1 - H. Buxton, 2003 Learning and understanding dynamic scene activity: a review. Image and Vision Computing, 21 1 125136 .
2 - G. Lu, X. Li, H. Li, 2008 Research on Recognition for Facial Expression of Pain in Neonates. ACTA Optica Sinica, 28 11 21092114
3 - P. Ekman, W. V. Friesen, 1978 Facial Action Coding System (FACS). Consulting Psychologist Press, Inc.
4 - M. Grimm, D. Dastidar, K. Kroschel, 2006 Recognizing Emotion in Spontaneous Facial Expressions. International Conference on Intelligent Systems and Computing
5 - W. Liu, Z. Wang, 2006 Facial Expression Recognition Based on Fusion of Multiple Gabor Features. The 18th International Conference on Pattern Recognition
6 - G. Littlewort, M. Bartlett, I. Fasel, et al. 2003 Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification. Neural Information Processing System,
7 - B. Lee, J. Chun, P. Park, 2008 Classification of Facial Expression Using SVM for Emotion Care Service System. The 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.
8 - I. Kotsia, N. Nikolaidis, I. Pitas, 2007 Facial Expression Recognition in Video Using a Novel Multi-class Support Vector Machines Variant. ICASSP
9 - P. Belhumeur, J. Hespanha, D. Kriegman, 1997 Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell, 19(7):711-720,
10 - H. Deng, J. Zhu, M. Lyu, I. King, 2007 Two-stage Multi-class AdaBoost for Facial Expression Recognition.Proceedings of International Joint Conference on Neural Network,
11 - Y. Zhu, C. Silva, C. Ko, 2002 Using moment invariants and hmm in facial expression recognition. Pattern Recognition Letters, 23(1-3):83-91,
12 - X. Mao, Y. Xue, Z. Li, K. Huang, S. Lv, 2009 Robust Facial Expression Recognition Based on RPCA and AdaBoost. WIAMIS
13 - Z. Ying, X. Fang, 2008 Combining LBP and Adaboost for Facial Expression Recognition. ICSP.
14 - S. Jung, D. Kim, K. An, M. Chung, 2005 Efficient Rectangle Feature Extraction for Real-time Facial Expression Recognition based on AdaBoost. International Conference on Intelligent Robots and Systems,.
15 - Bouchrika, 2008 Gait Analysis and Recognition for Automated Visual Surveillance. School of Electronics and Computer Science, University of Southampton,
16 - S. Lajevardi, M. Lech, Facial Expression Recognition from Image Sequences Using Optimized Feature Selection.
17 - P. Rani, N. Sarkar, J. Adams, 2007 Anxiety-based affective communication for implicit human machine interaction. Advanced Engineering Informatics. 21 2007 323334
18 - H. Kage, M. Seki, K. Sumi, K. Tanaka, K. Kyuma, Pattern Recognition for Video Surveillance and Physical Security. SICE Annual Conference
19 - C. Huang, C. Wang, 2006 A GA-based feature selection and parameters optimization for support vector machines. Expert System with Applications, 31:231:240
20 - L. Tang, Y. Zhou, J. Jiang,, 2007 Radius Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies. J.Chen. Inf.Model, 47 14381445
21 - F. Melgani, Y. Bazi, 2008 Classification of Electrocardiogram Signals with Support Vector Machines and Particle Swarm Optimization. IEEE Trans. On Information and Technology in Biomedicine, 12(5):667-677,
22 - Z. Liu, C. Wang, S. Yi, 2009 A combination of modified particle swarm optimization algorithm and support vector machine for Pattern Recognition. The 3rd International Symposium on Intelligent Information Technology Application
23 - S. Romdhani, P. Torr, B. Acholkopf, A. Blake, 2004 Efficient face detection by a cascaded support vector machine expansion. Proceedings of the Royal Society,
24 - R. Eberhart, J. Kennedy, 1995 A new optimizer using particle swarm theory. The sixth international symposium on micro machine and human science,:3943
25 - Y. Shi, R. C. Eberhart, 1998 Proceedings of IEEE International Conference on Evolutionary Computation,,6973
26 - R. T. Collins, A. J. Lipton, T. Kanade, et al. 2000 A system for Video Surveillance and Monitoring. Technical Report CMU-RI-TR-0012 , Carnegie Mellon University,
27 - Visual Surveillance System 10 An intelligent, turn-key, enterpriser-wide, visual surveillance system for any sized security installation,
28 - R. Picard, 1997 Affective computing. Cambridge, MA: MIT Press,
29 - C. Kim, S. Pang, M. Je, 2005 Constructing support vector machine ensemble. Pattern Recognition, (36):2757-2767
30 - I. Buciu, 2006 Demonstrating the stability of support vector machines for classification. Signal Processing. (86): 2364-2380.
31 - W. Jeevani, 2001 Performance Degradation in Boosting. In conf. MCS:multiple classifier systems,1121 .
32 - J. Yang, V. Honvar, 1998 Feature subset selection using a genetic algorithm. IEEE Intelligent System and their Application, 13(2),44-49,
33 - C. L. Huang, C. J. Wang, 2006 A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications. 31 231 240
34 - C. W. Hsu, C. C. Chang, C. J. Lin, 2003 A pratical guide to support vector classification. Available at:
35 - R. Wang, Z. Hu, L. Chen, J. Xiong, 2010 An Approach on Feature Selection and Parameters Optimization of Cascaded SVM with Particle Swarm Optimization Algorithm. The 3rd International Workshop on Computer Science and Engineering (WCSE),
36 - J. Kennedy, R. Eberhart, 1995 Particle Swarm Optimization. Proceedings of IEEE international Conference on Neural Networks, IEEE Service Center, Piscataway, NJ,
37 - R. Eberhart, J. Kennedy, 1995 A new optimizer using particle swarm theory. The sixth international symposium on micro machine and human science,:3943
38 - Y. Shi, 2004 Particel Swarm Optimization. IEEE Neural Network Society, February
39 - Y. Shi, 1999 Russel Eberhart. A modified Particel Swarm Optimizer. Proceedings of the IEEE congress on Evolutionary Computation (CEC 1999 6973 , Piscataway NJ,
40 - L.-J. Tang, Y.-P. Zhou, et al. 2007 Radius Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies[J]. J. Chem. Inf. Model.,47 14381445
41 - J. Yang, 2006 An improved cascade SVM training algorithm with crossed feedbacks. Proceedings of the First International multi-symposiums on Computer and Computatioal Sciences,
42 - Y. Ma, X. Ding, 2002 Face Detection based on Cost-sensitive Support Vector Machines. Lecture Notes in Computer Science, Springer-Verlag Berlin,
43 - D. Iakovidis, I. Flaounas, S. Karkanis, D. Maroulis, 2004 A cascading support vector machines system for gene expression data classification. Second IEEE international conference on intelligent system, june
44 - G. C. Cawley, N. Talbot, 2003 Efficient leave-one-out cross validation of kernel Fisher discriminant classifiers. Pattern Recognition. 36 11 2585-2592,