FACIAL EXPRESSION RECOGNITION WITH AUTO-ILLUMINATION CORRECTION

2020

Emotions are very present in our daily life. They are manifested by physiological reactions (palpitations, heat, and acceleration of the pulse), motor reactions (gestural expressions, facial expressions) and changes in the voice. The complexity of emotions was capable to seek attention of various researchers. Differing representations of emotions were then proposed to produce a set of theories. The automation of facial expression detection can then be approached in a variety of ways. We concentrated on the face expression detection. Concerning to this the recognition is based on different visual characteristics of the expression. This paper is dedicated at first to the exhibition of the definition of emotions, followed by the great theories that have emerged in this area. In a second step, we present the methods of extraction of the facial characteristics and the methods of selection. Finally, some databases used for automatic recognition of emotions are presented.

Optik, 2018

Automatic facial expression recognition has always been a challenging task to understand human behavior from real world images. Certain type of issues are associated with such images that include poor illumination, different orientations and varying pose. The proposed technique first applies Fast Fourier Transform and Contrast Limited Adaptive Histogram Equalization (FFT + CLAHE) method to compensate the poor illumination. Then merged binary pattern code (MBPC) is generated for every pixel. Two bits per neighbourhood are produced to form a 16-bit code per pixel. This code merges local features to improve the effectiveness of facial expression recognition system. MBPC descriptor captures changes along fine edges and prominent pattern around eyes, eye brows, mouth, bulges and wrinkles of the face. The results of proposed technique are compared with different variants of LBP and LGC based techniques for both holistic and zoned images. Static Facial Expression in Wild (SFEW) dataset is selected for experimentation. Results clearly indicate that the suggested MBPC based technique surpasses other techniques with 96.5% and 67.2% accuracy for holistic and division based approach respectively. Moreover, results indicate that the performance of holistic approach is much higher than division based approach.

The primary goal of this paper is to make a comparative study on various approaches used to identify the person's face and thereafter recognize their emotion. Among the various techniques implemented, Neural Networks, Hidden Markov Model and Dimensionality reduction techniques have received lot of attention. The face and facial emotion recognition system would require care and efforts in data acquisition, pre-processing, feature extraction, classification and performance evaluation. The main aim of this review paper is to study and compare the well-known techniques used at different stages to recognize the face and its emotion. different gender, race and features. These varieties in the database such as glasses, moustache and beard had enabled the system to learn to differentiate various levels of facial expressions successfully.

International journal of engineering research and technology, 2013

Since the early ninetees, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging human–computer interfaces, talking heads, image retrieval and human emotion analysis. Facial expressions reflect not only emotions, but other mental activities, social interaction and physiological signals. There have been several advances in the past few years in the areas of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper is a survey of some of the published works since 2001 till date. In this survey, the most prominent automatic facial expression analysis methods and systems presented in the literature are discussed. Facial motion and deformation extraction approaches as well as classification methods are discussed with respect to issues such as face normalization, facial expression dynamics and intensity and also with regard to their robustnes...

Facial Expression conveys non-verbal cues, which plays important roles in interpersonal relations. The Facial Expression Recognition system is the process of identifying the emotional state of a person. In this system captured image is compared with the trained dataset available in database and then emotional state of the image will be displayed. This system is based on image processing and machine learning. For designing a robust facial feature descriptor, we apply the Local Binary Pattern. Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The histogram will be formed by using the operator label of LBP. The recognition performance of the proposed method will be evaluated by using the trained database with the help of Support Vector Machine. Experimental results with prototypic expressions show the superiority of the LBP descriptor against some well-known appearance-based feature representation methods. We evaluate our proposed method on the JAFFE and COHN-KANADE dataset. The Precision, Recall and Fscore from the COHN-KANADE dataset were 83.6142%, 95.0822% and 88.9955% respectively and that of JAFFE dataset were 91.8986%, 98.3649%, 95.0218% respectively. Experimental results demonstrate the competitive classification accuracy of our proposed method.

2004

In this paper, the integration of face feature detection and extraction, and facial expression recognition are discussed. In this paper, we propose an algorithm that utilizes multi-stage integral projection to extract facial features. Furthermore, in this project, we propose a statistical approach to process the optical flow data to obtain the overall value for the respective feature region in the face. This approach has eliminated the requirement of accurate identification of the feature boundary. Optical flow computations are utilized to identify the directions and the amount of motions in image sequences that are caused by human facial expressions. The optical flow computation results are processed using Kalman filtering. The filtered results are given to a neural network to realize a mapping into the facial expression space. This technique is used on a set of training and testing face images. Preliminary experiments indicate an accuracy between 60% -80% on the Kalman filtered data when recognizing four types of expressions: anger, sad, happy and surprise. In an attempt to further improve the recognition results, we proposed a technique to process the optical flow results using a statistical approach instead of using Kalman filtering. The preliminary experiments on this proposal approach produced accuracy between 70% -100% on the original optical flow results that is better than the Kalman filter technique.

The automatic recognition of facial expressions has been an active research topic since the early nineties. There have been several advances in the past few years in terms of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper surveys some of the published work since 2001 till date. The paper presents a time-line view of the advances made in this field, the applications of automatic face expression recognizers, the characteristics of an ideal system, the databases that have been used and the advances made in terms of their standardization and a detailed summary of the state of the art. The paper also discusses facial parameterization using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs) and the recent advances in face detection, tracking and feature extraction methods. Notes have also been presented on emotions, expressions and facial features, discussion on the six prototypic expressions and the recent studies on expression classifiers. The paper ends with a note on the challenges and the future work. This paper has been written in a tutorial style with the intention of helping students and researchers who are new to this field.

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.

Facial feature extraction is an important preprocessing step in facial expression recognition. The performance of facial feature extraction can greatly influence the accuracy of the recognition system. In this paper, an automated facial feature extraction method that is tolerant to cluttered scene, illumination change and depth is proposed. The method first utilizes skin color to swiftly detect the face in the image and then proposes a hierarchical geometrical framework to estimate the range of facial features. An adaptive gradient mask which can resist to illumination change and adapt to deformation of facial features is devised. Experimental results show that the method can extract exact locations of deformed facial features in five kinds of facial expressions.

Expert Systems with Applications, 2009

Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%.