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.
In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems, we adopt an improved PCA to extract the features and uses the random forest algorithm as the classifier. The experiments are conducted based on the JAFFE facial expressions library, and are compared with the support vector machine (SVM) classifier. Theoretical analysis and experimental results show that the facial expression recognition system of random forest based on improved PCA extracting feature, whether in terms of facial expression recognition rate or running speed, has shown significant improvement. 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. Emotion elicitation and classification have been performed on standardized stimuli sets, such as international affective picture systems and international affective digital sound. However, the literature which elicits and classifies emotions in a financial decision making context is scarce. In this paper, we present an evaluation to detect emotions of private investors through a controlled trading experiment. Subjects reported their level of rejoice and regret based on trading outcomes, and physiological measurements of skin conductance response and heart rate were obtained. To detect emotions, three labeling methods, namely binary, tri-, and tetrastate blended models were compared by means of C4.5, CART, and random forest algorithms, across different window lengths for heart rate. Taking moving window lengths of 2.5s prior to and 0.3s postevent (parasympathetic phase) led to the highest accuracies. Comparing labeling methods, accuracies were 67 for binary rejoice, 44 for a tristate, and 45 for a tetrastate blended emotion models. The CART yielded the highest accuracies. 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.