Subject-independent Facial Expression Recognition Research Articles

A Noval Distributed Architecture for Expression Recognition

A distributed facial expression recognition approach based on MB-LGBP feature and decision fusion is presented in this paper to accomplish subject-independent facial expression recognition more efficiently. At first, the Multi-scale Block Local Gabor Binary Patterns (MB-LGBP) are extracted from expression regions to achieve both locally and globally informative features. Then a distributed architecture is proposed to accelerate the recognition process, in which features of each single region are utilized to perform expression classification in parallel. The final decision is made by an artificial neuron network (ANN) based data fusion of the confidence information got from the classification of each region. In experiment, we compare the runtime and recognition accuracy of our system with several other popular expression recognition paradigms. The results show that the distributed architecture can promote the efficiency of facial expression recognition prominently with comparative performance in recognition accuracy.

Expression recognition based on 2D multi-scale block local Gabor binary patterns

In order to accomplish subject-independent facial expression recognition,a facial expression recognition approach based on 2D Multi-scale Block Local Gabor Binary Patterns(2D MB-LGBP)was presented.MB-LGBP features have been proved to be both locally and globally informative for expression recognition.This research combined the idea of MB-LGBP with the concept of Gray Level Co-occurrence Matrix(GLCM)to achieve the 2D MB-LGBP features,which can encode the local textures with structure information.In recognition,SVM classifier was utilized and its performance was compared with the traditional weighted Chi-square distance based paradigm.The experimental result proves the superiority of the 2D MB-LGBP composite features to MB-LGBP and some other popular features in expression recognition.

FACIAL EXPRESSION RECOGNITION BASED ON GABOR WAVELET TRANSFORMATION AND ELASTIC TEMPLATES MATCHING

Facial expression recognition technology plays an important role in research areas such as psychological studies, image understanding and virtual reality etc. In order to achieve subject-independent facial expression recognition and obtain robustness against illumination variety and image deformation, facial expression recognition methods based on Gabor wavelet transformation and elastic templates matching are presented in this paper. First given a still image containing facial expression information, preprocessors are executed which include gray and scale normalization. Secondly, Gabor wavelet filters are adopted to extract expression features. Then the elastic graph for expression features is constructed. Finally, elastic templates matching algorithm and K-nearest neighbors classifier are used to recognize facial expression. Experiments show that expression features can be extracted effectively by Gabor wavelet transformation, which is insensitive to illumination variety and individual difference, and high recognition rate can be obtained using elastic templates matching algorithm, which is subject-independent.

Subject independent facial expression recognition with robust face detection using a convolutional neural network

Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.