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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.

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.

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).

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.

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.

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.