Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection

In this paper, a new approach for facial expression recognition has been proposed. The approach has imbedded a new feature extraction technique, new multiclass classification approach and a new kernel parameter optimization for support vector machines. The scheme of the approach begins with feature extraction from the input vectors, and the extracted features are transformed into a Gaussian space using compressive sensing techniques. This process ensures feature vector dimensionality reduction and matches the features vectors with radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training is automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. With the optimized kernel parameter, new proposed multiclass classification approach is used to finally classify any vector. In all the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings. The results further confirmed the validity and advantages of the proposed approach over other approaches currently used in the literature.

Facial expression recognition with dynamic cascaded classifier

In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multiresolution approach to facial expression problems. Initially, each image sample is decomposed into desired pyramid levels at different sizes and resolutions. Pyramid features at all levels are concatenated to form a pyramid feature vector. The vectors are further reinforced and reduced in dimension using a measurement matrix based on compressive sensing theory. For classification, a multilevel classification approach based on single-branch decision tree has been proposed. The proposed multilevel classification approach trains a number of binary support vector machines equal to the number of classes in the datasets. Class of test data is evaluated through the nodes of the tree from the root to its apex. The results obtained from the approach are impressive and outperform most of its counterparts in the literature under the same databases and settings.

Facial expression recognition based on image pyramid and single-branch decision tree

The paper proposes an efficient human expression recognition method in transformed domain using discrete Contourlet transform (DCT). DCT represents smooth contour information in different directions reflecting human perception and so relevant to recognize facial expressions more accurately. Each face is decomposed using discrete Contourlet transform up to fourth level and coefficients of high frequency and low frequency components with varied scales and angles are obtained. Logarithmic invariant moments of directional coefficients at different levels and histogram analysis are used to build the feature vectors for classification. To reduce dimension of the feature vectors, directional subbands are selected by analyzing the entropy of the feature vectors. Support Vector Machine (SVM) is applied to classify different expressions using the proposed method. Experimental results show promising performance applied on JAFFE and Cohn-Kanade database compare to other transformed domain methods.

An Efficient Expression Recognition Method using Contourlet Transform

A novel facial expression recognition method based on discrete shearlet transform is proposed in this paper, which is a new image multiscale geometric analysis method. In addition to multi-resolution and time-frequency localization owned by wavelet transform, the shearlet transform also has anisotropy and directionality. Firstly, normalization and equalization are applied to all test and training images. Then the facial expression features are extracted based on discrete shearlet transform, and Vector Support Machine is used to classify the seven expressions (happiness, sadness, surprise, disgust, fear, anger and neural) of JAFFE database. Experimental results show that the recognition rate of the proposed method is improved comparing to several known methods.

A novel approach of facial expression recognition based on shearlet transform

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.

Lin-Bo Cai

Papers

A new approach of facial expression recognition based on Contourlet Transform