Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85 to 98.57 for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.
The classification of the extracted features can be implemented using Support Vector Machine (SVM) algorithm @cite_26 @cite_12 , K-Nearest Neighbor (KNN) method @cite_36 @cite_22 , Random Forest classification method @cite_25 @cite_34 and Gaussian process @cite_9 . The SVM principle is based on non-linear mapping and identification of a hyperplane for the separation of data classes. SVM classifier is used with the application of different kernel functions, such as linear, quadratic, polynomial and radial basis functions, to optimize the SVM performance @cite_26 @cite_12 . KNN approach is based on the numerical comparison of a testing data sample with training data samples of each class followed by the determination of the similarity scores. The data class is defined by K most similar data samples based on the minimum difference between train and test data @cite_36 @cite_22 . KNN and SVM classifiers are simple and widely used for emotion recognition, however these classifiers do not suppress the outliers that leads to lower recognition accuracy.
@cite_26 @cite_22 @cite_36 @cite_9 @cite_34 @cite_25 @cite_12
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Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71 by using leave-one-out strategy and 94.13 by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256. Facial emotion recognition is the most significant parameter for an efficacious Human Machine Interaction (HMI). It plays a crucial role in interpreting and communicating with the people who have speaking impairments as well as a medium to understand and communicate with infants who cannot emote their feelings verbally. In this paper, we propose a hybrid feature extraction technique consisting of Discrete Wavelet Transform (DWT) accompanied by Shape Feature Extraction Technique (SFET).The features extracted were tested on standard classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbourhood (KNN) classifiers. Facial images from JAFFE and Cohn-Kennedy databases were utilized for training as well as testing. The work shows a very high facial emotion recognition rate of 93.94 and 91.8 with the proposed method for JAFFE and Cohn-Kanade databases respectively. Facial expressions are a valuable source of information that accompanies facial biometrics. Early detection of physiological and psycho-emotional data from facial expressions is linked to the situational awareness module of any advanced biometric system for personal state re identification. In this article, a new method that utilizes both texture and geometric information of facial fiducial points is presented. We investigate Gauss–Laguerre wavelets, which have rich frequency extraction capabilities, to extract texture information of various facial expressions. Rotation invariance and the multiscale approach of these wavelets make the feature extraction robust. Moreover, geometric positions of fiducial points provide valuable information for upper lower face action units. The combination of these two types of features is used for facial expression classification. The performance of this system has been validated on three public databases: the JAFFE, the Cohn-Kanade, and the MMI image. The Gaussian process (GP) approaches to classification synthesize Bayesian methods and kernel techniques, which are developed for the purpose of small sample analysis. Here we propose a GP model and investigate it for the facial expression recognition in the Japanese female facial expression dataset. By the strategy of leave-one-out cross validation, the accuracy of the GP classifiers reaches 93.43 without any feature selection extraction. Even when tested on all expressions of any particular expressor, the GP classifier trained by the other samples outperforms some frequently used classifiers significantly. In order to survey the robustness of this novel method, the random trial of 10-fold cross validations is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application. Affective computing endows computers with the ability to observe and understand, thus generates a variety of emotional characteristics. Facial expression has continuous signal which is direct expression of emotion. Affective computing via facial expressions has aroused great interest in intelligent interactive and pattern recognition recently. RGB camera is sensitive to surroundings, especially to illumination conditions. 2D images are not robust to human faces which are 3D objects. In this paper, we propose a method that uses noisy depth data produced by the low resolution sensor for robust face recognition. We capture color and depth information by Kinect sensor, extract Facial Feature Points (FFPs) feature vector by Face Tracking SDK, and recognize facial expression by Random Forest (RF) algorithm. This method enables facial expression recognition implementation real-time in intelligent interactive. Patients with Parkinsons Disease (PD) frequently exhibit deficits in the production of emotional speech. In this paper, we examine the classification of emotional speech in patients with PD and the classification of PD speech. Participants were recorded speaking short statements with different emotional prosody which were classified with three methods (naive Bayes, random forests, and support vector machines) using 209 unique auditory features. Feature sets were reduced using simple statistical testing. We achieve accuracies of 65.5 and 73.33 on classifying between the emotions and between PD vs. control, respectively. These results may assist in the future development of automated early detection systems for diagnosing patients with PD. Affective computing has become a growing field of research activities due to its wide use of application in human computer interface. Emotion recognition is one of the state-of-the-art techniques in determining current psychological state of human being. Human emotions are very overlapping in nature and thus it needs an efficient feature-extractor and classifier assembly. This paper reports a novel non-invasive technique to classify human emotion through thermal images of face. Hu's moment invariants of different patches have been fused with histogram statistical feature and used as robust features in multiclass support vector machine based classification. It is found that emotions from thermal image can be classified by the proposed method with a satisfactory performance.