PCA facial expression recognition

2003

Two hybrid systems for classifying seven categories of human facial expression are proposed. The £rst system combines independent component analysis (ICA) and support vector machines (SVMs). The original face image database is decomposed into linear combinations of several basis images, where the corresponding coef£cients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values. The classi£cation accuracy of this system is compared against that of baseline techniques that combine ICA with either two-class cosine similarity classi£ers or two-class maximum correlation classi£ers, when we classify facial expressions into these seven classes. We found that, ICA decomposition combined with SVMs outperforms the aforementioned baseline classi£ers. The second system proposed operates in two steps: £rst, a set of Gabor wavelets (GWs) is applied to the original face image database and, second, the new features obtained are classi£ed by using either SVMs or cosine similarity classi£ers or maximum correlation classi£er. The best facial expression recognition rate is achieved when Gabor wavelets are combined with SVMs.

International Journal of Computer Applications, 2013

Facial expression recognition (FER) has good applications in different aspects of day-today life. But not yet realized due to unavailability of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction by using feed-forward neural networks (classifier) for recognition of four different facial expressions from still pictures of the human face. The study presented here gives simple method in facial expression recognition. The study presented here gives 72.50% recognition of facial expression for the entire database of JAFEE. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions.

International Journal of Computer Applications, 2014

Facial Expression Recognition has always been topic of interest for research in field of human computer/machine interaction based on machine learning and image processing tools. However, the inaccuracy of such systems is always challenging. Hence to resolve this problem, a novel algorithm using Gabor Filter and Eigenvector Based Distributed Feature, is proposed in this paper. The work is enhancement of existing Face Identification by covering the challenges of Facial Expression. The approach combines face recognition along with facial expression recognition. Over the time authentication system is replaced by biometric authentication. These systems are based on some human features and behavior. The mean accuracy is improved to 93.4%. The effectiveness and stability is shown in experimental results of the algorithm.

Artificial Life and Robotics, 2011

Facial expression recognition has recently become an important research area, and many efforts have been made in facial feature extraction and its classification to improve face recognition systems. Most researchers adopt a posed facial expression database in their experiments, but in a real-life situation the facial expressions may not be very obvious. This article describes the extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions. The objective of our research was to investigate the performance of a facial expression recognition system with a minimum number of features of the Gabor wavelet. In this research, principal component analysis (PCA) is employed to compress the Gabor features. We also discuss the selection of the minimum number of Gabor features that will perform the best in a recognition task employing a multiclass support vector machine (SVM) classifier. The performance of facial expression recognition using our approach is compared with those obtained previously by other researchers using other approaches. Experimental results showed that our proposed technique is successful in recognizing natural facial expressions by using a small number of Gabor features with an 81.7% recognition rate. In addition, we identify the relationship between the human vision and computer vision in recognizing natural facial expressions.

Facial expression recognition is a major task concerning human-computer interaction issue. Plenty of techniques were proposed to recognize an expression either in still images or image sequences. However, most of them were applied for images recorded under controlled recording conditions. This paper aims at describing Gabor filters' application to extract facial features required to classify facial expression when the images are disturbed by various noise levels. The experiments indicate a satisfactory performance for Gabor filters when compared to another state-of-the-art method named principal component analysis (PCA).

The automatic facial expression recognition (FER) system is an important concept in human computer interface (HCI), as human face is a part that gives information about the state of user's behavior through various expressions. This study of recognizing facial expression is one of the challenging research areas in image analysis and computer vision. Since last 2 decades many researchers are working to make HCI machines to operate with more reliability and efficiency even in the worst conditions. In this paper, we studied different FER methods such as face detection, feature extraction and expression classification where techniques like Knowledge-based, Feature-based, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor filters, Local Binary Patterns (LBP) with a range of classifiers like a SVM, Adaboost, HMM etc are been compared.

2014

The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint. The process of recognizing facial expression is a hard task due to the several circumstances such as facial occlusions, face shape, illumination, face colors, and etc. This paper present a PCA methodology to distinguish expressions of faces under different circumstances and identifying it. Relies on Eigen faces technique using standard Data base images. So as to overcome the problem of difficulty to computers to identify the features and expressions of persons.

2014

database, sequentially pre processing on every image of database is done. Then from those images features are extracted using Gabor Filters. In this paper, JAFFE database is used. It consists of six basic facialexpression emotions. The JAFFE database is used totrain and evaluate algorithm. In this work, Globalfeature based matching method is used that is whole face is used as input to recognition system. The proposed system will developed by local feature based matching method, local features such as eyes, mouth extraction using Gabor filter. The SVM will be used for features classification.

2012

Facial expression recognition has potential applications in different aspects of day-today life not yet realized due to absence of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction in combination feedforward neural networks (classifier) for recognition of seven different facial expressions from still pictures of the human face. The study presented here gives simple method in facial expression recognition. The study presented here gives 60% to 70 % recognition of facial expression for the entire database of JAFEE. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions. The present study proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and human computer interaction.

Machine Vision, 2009. …, 2010