Detecting Facial Expression in Images

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.

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016

Facial expression recognition performs a critical role in the human-machine interaction area. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Facial Expression Recognition system is used in security. During the past years recognition of face has received most important attention as one of the most significant image application understanding and analysis. Many algorithms have been implemented on different static and non static conditions (uncontrolled conditions).Static conditions include static and uniform background, identical poses, similar illumination, neutral frontal face .Non static conditions include position, partial occlusion orientation; varying lighting conditions and facial hair, which makes recognition process a complex problem. Facial expression recognition is a example where computer and humans underperforms. It has most importance for the video retrieval and video based management classification. It may be used in behavior psychology and science.

Computer Science and Information Technologies

Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

2013

This paper proposes a emotion detector, applied for facial images, based on the analysis of facial segmentation. The parameterizations have been developed on spatial and transform domains, and the classification has been done by Support Vector Machines. A public database has been used in experiments, The Radboud Faces Database (RAFD), with eight possible emotions: anger, disgust, fear, happiness, sadness, surprise, neutral and contempt. Our best approach has been reached with decision fusion, using transform domains, reaching an accurate up to 96.62%.

Procedia Computer Science, 2015

Face Expression plays an important role in human communication. Facial Expression Recognition (FER) is process performed by computers which consist of detect the face in the image and preprocess the face region, extracting facial expression features from image by analyzing the motion of facial features or change in the appearance of facial features and classifying this information into facial expression categories like prototypic facial expression such as fear, happy, sad or Action Units(AU) such as eye open or mouth stretched. Face Expression Recognition techniques have always been a very challenging task in real life applications because of the variations in the illumination, pose and occlusion. This paper presents a methodology for face expression recognition.

The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher's interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed.

In this paper, we have provided a comprehensive review of modern facial expression recognition system. The history of the technology as well as the current status in terms of accomplishments and challenges has been emphasized. First, we highlighted some modern applications of the technology. The best methods of face detection, an essential component of automatic facial expression system, are also discussed. Facial Action Coding Systems-the cumulative database of research and development of micro expressions within the behavioral science are also enlightened. Then various facial expression databases and the types of recognitions are explained in detail. Finally, we provided the procedures of facial expression recognition from feature extraction to classifications, emphasizing on modern and best approaches. Then the challenges encountered when comparing results with others are highlighted and suggestions to alleviate the problems, provided.

Computer vision is one among the thrust research area in the field of Image processing. Facial expression recognition is one among the thrust research dimension in computer vision. The process of recognition and identification is important due to the similarity of facial expressions. The motivation behind this research area is its capability to resolve an image processing problem and its wide range of applications. The main rationale of all images processing and computer vision algorithms is to build the visual data in a useful manner. For this reason in the domain of computer vision, the facial expression recognition begins with its applications in the HCI (Human Computer Interaction) where visual look of human, sight and touch sensations (also known as moods) and voice are utilized at the same time. This research article reviews several literatures pertaining to facial expression recognition.

The system-based identification of facial expressions has been a vital area of research in the literature for a long time. The most meaningful way humans exhibit emotions is byfacial expressions. The presented work confers a new framework for facial expressions recognition from video files by selectingthe Gabor features on video frames. A set of dimensionality reduction technique, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used to minimize the computational complexity. These feature vectors are compared with the trained vectors of different facial expressions in the database and finally, a Support Vector Machine is used to classify different kinds of facial expressions belonging to the face video frames. This method reveals a better performance ratio.

2013

In recent year facial expression recognition system has gained much popularity in the field of security because facial expression recognition system integrates with the face recognition system make entrenched and improve its strength. Automatic recognition of facial expression achieves greater attraction of researcher and human facial expressions have the ability to communicate emotion and play a major role in the study of psychological phenomena and the progress of nonverbal communication. This paper present facial expression detection method by first skin color segmentation for detecting face and then extract facial feature like eyes and lips after finding the interest point on these facial landmark. Then drawing Bezier curve of these facial points and on the basis of changes in muscles around the eyes, nose and mouths we recognize the three basic facial expressions and achieved expected results. Keywords— Facial expression recognition, Bezier curve, Haar like feature, corner poin...