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This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

This paper proposed a new face recognition algorithm based on Haar-Like features and Gentle Adaboost feature selection via sparse representation. Firstly, All the images including face images and non face images are normalized to size and then Haar-Like features are extracted . The number of Haar-Like features can be as large as 12,519. In order to reduce the feature dimension and retain the most effective features for face recognition, Gentle Adaboost algorithm is used for feature selection. Selected features are used for face recognition via sparse representation classification (SRC) algorithm. Testing experiments were carried out on the AR database to test the performance of the new proposed algorithm. Compared with traditional algorithms like NS, NN, SRC, and SVM, the new algorithm achieved a better recognition rate. The effect of face recognition rate changing with feature dimension showed that the new proposed algorithm performed a higher recognition rate than SRC algorithm all the time with the increasing of feature dimension, which fully proved the effectiveness and superiority of the new proposed algorithm.

A new method based on Monogenic Binary Coding (MBC) is proposed for facial expression feature extraction and representation. Firstly, monogenic signal analysis is used to extract multi-scale magnitude, orientation and phase components. Secondly, Monogenic Binary Coding (MBC) is used to encode the monogenic local variation and intensity in local regions of each extracted component in each scale and local histograms are built. Then Blocked Fisher Linear Discrimination (BFLD) is used to reduce the dimensionality of histogram features and to enhance discrimination. Finally the three complementary components are fused for more effective facial expression recognition (FER). Experiment results on Japanese female expression database (JAFFE) show that the performance of the fusion method is even better than state-of-the-art local feature based FER methods such as Local Binary Pattern (LBP)+Sparse Representation (SRC), Local Phase Quantization (LPQ)+SRC ,etc.

This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

Sparse Representation-based Classification (SRC) is a newly introduced algorithm for face recognition, notable for its robust performance to occlusions and corruptions. Local Binary Patterns (LBP) is a very powerful method to describe the texture and shape of images. In this paper, we propose a novel method for facial expression recognition based on sparse representation of LBP features. Extensive experiments on Japanese Female Facial Expression (JAFFE) database are conducted. The experiment results show that the new method has a better performance than using Sparse Representation-based Classification solely on facial recognition, and is also better than those traditional algorithms such as Principal Component Analysis (PCA) and Linear discriminant analysis (LDA).

Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via SRC. Extensive experiments of FER via SRC algorithm are carried out to study the performance of sparse representation theory for FER. The comparison of SRC algorithm with various traditional algorithms such as two-dimensional PCA as well as curvelet transform for FER is also given. Support vector machine is used for expression classification. The paper also studies the robustness of SRC algorithm for FER to noises. Satisfactory results are obtained for FER via sparse representation. The experiment results show the effectiveness of SRC algorithm on FER.

In this paper, A new approach to facial expression recognition is constructed by combining the support vector discriminant analysis (SVDA) and local binary pattern (LBP) operator. LBP is an effective low-cost image descriptor to extract facial texture representing expression features. The basic idea of SVDA is to find the projection axes according to the margin maximization criterion. SVDA is an excellent data dimension reduction method which benefits from the intrinsic merits of SVM such as generalization abilities and kernel tricks for nonlinear classification. The proposed algorithm is experimented on the Japanese female facial expression (JAFFE) database. Extensive experiments are carried out to compare with other common methods such as PCA and LDA. The experiment results show that the combination of LBP+SVDA provides better performance than that of those traditional algorithms and prove the effectiveness of the proposed algorithm.

Contourlet Transform provides a flexible multiresolution, local and directional image expansion and a sparse representation for two dimensional piecewise smooth signal resembling images. It overcomes the weakness of wavelet transform in dealing with high dimensional signals. In this paper, the theory of Contourlet Transform is introduced and a new approach of facial expression recognition based on Contourlet Transform is proposed. Locally Linear Embedding is then applied for feature dimensionality reduction, and Vector Support Machine is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) of JAFFE database. Both Wavelet Transform and Principal Component Analysis (PCA) algorithm are carried out to compare with the approach proposed. Experimental results show that the proposed approach gets better recognition rate than both Wavelet Transform and Principal Component Analysis. The facial expression recognition based on Contourlet Transform is an effective and feasible algorithm.

In this paper, the facial expression recognition (FER) is investigated based on the observation that a sequence of images of a certain facial expression define a smooth manifold. First, local binary pattern (LBP) algorithm is used to extract the local texture features of the expression images. Then, locally linear embedding (LLE) method is used to learn the structure of the expression manifold in the LBP feature speace. Finally support vector machine (SVM) is used for the classification of expressions. The LBP+LLE algorithm is experimented on the Japanese female facial expression (JAFFE) database. Extensive experiment result comparisons show that LBP features and manifold approach are effective methods for FER. Their combination provides much better performance compared with that of those traditional algorithms such as PCA, LDA, etc.

In this paper, A new approach to face recognition is constructed by combining the local binary pattern (LBP) operator and locally linear embedding (LLE). LBP is an effective low-cost image descriptor to extract facial texture feature which represents the local structure of face images. LLE is an excellent non-linear data dimensionality reduction method. Its main optimization only involves a sparse eigenvalue problem and do not involves local minima. The new approach benefits from the advantages of both LBP and LLE. The proposed algorithm is experimented on ORL database. Extensive experiments are carried out to compare with other common methods such as LDA and LLE. The experiment results show that the combination of LBP+LLE provides better performance than that of those traditional algorithms and prove the effectiveness of the proposed algorithm.

In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented, and used for gender classification. In 2DHDA, equal within-class covariance constraint is removed. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, the criterion of 2DHDA, log and rearranging terms are taken, and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly, face images are projected onto the optimal projection matrix, thus the 2DHDA features are extracted. Finally, Nearest Neighbor classifier is selected to perform gender classification. Experimental results show that higher recognition rate is obtained by way of 2DHDA compared with 2DLDA and HDA.

A novel approach to facial expression recognition with Marginal Fisher Analysis (MFA) on Local Binary Pattern (LBP) is proposed. Firstly, each image is transformed by an LBP operator and then divided into 3x5 non-overlapping blocks. The features of facial expression images are formed by concatenating the LBP histogram of each block. Secondly, MFA algorithm based on Graph Embedding (GE) is applied for dimensionality reduction. Finally, Support Vector Machine (SVM) is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) on Japanese Female Facial Expression (JAFFE) database. The maximum facial expression recognition rate of the proposed algorithm reaches to 65.71% for person-independent expression recognition, which is better than LBP+LDA algorithms. The experiment results prove that the facial expression recognition with MFA on LBP is an effective and feasible algorithm.

In this paper new results on the performance comparison of compression technique based on wavelet and wavelet packet transformation on audio band signals (especially musical signals) are presented. In our performance study, the sensitivities of wavelet compression with different wavelet functions (filter type and length), data frame length, selection of minimum quantization level, basis selection technique and information cost functions are compared and analyzed. In general, our results serve as an indication in the selection of various wavelet parameters for audio band signal compression.