Facial Expression Recognition Technology Research Articles

Facial-Expression Recognition Technology Applying for Customer Service

Facial-Expression Recognition Technology Applying for Customer Service

A Survey on Facial Expression Recognition Technology and Its Use in Virtual Systems

Human Facial expressions are the most expressive way to display their non verbal emotions. Human can easily detect and interpret faces to understand the emotions of the person in front of them. An important aspect of facial expression recognition is its implementation in the virtual domain. Different apps for auto-face recognition can be an important factor for the growth of components of natural human-machine interfaces. Many attempts are made from few years for the development of automated systems for which will detect human face expressions and in turn their moods. This paper gives a survey over the expertise used for human moods detection and recognition of facial expression recently.

심리로봇적용을 위한 얼굴 영역 처리 속도 향상 및 강인한 얼굴 검출 방법

Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.

Facial Expression Recognition Based on RS-SVM

In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.

Facial Expression Recognition Based on Improved Dimension Reduction of Gabor Feature and Two-against-Two Multi-Class SVM Classification

The facial expression recognition technology has been widespread concerned and researched, and many methods have been presented. This paper focuses on studying and analyzing the feature extraction, feature dimension reduction and two-against-two multi-class Support Vector Machine (SVM) method, and an algorithm is proposed for recognition of six basic facial expressions. According to expression feature information in the different face region, the algorithm adopts local nonuniform feature point extraction to reduce the feature dimension. After transforming the feature points with Gabor filters, the Gabor expression features are obtained. And the feature dimension is further reduced by discrete wavelet transform (DWT) and discrete cosine transform (DCT). At last, the tow-against-two classification method and an optimum decision scheme are used to realize quick and accurate expression classification. Experimental results show the algorithm can achieve higher recognition rate, recognition speed and stronger robust.

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.