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 commonly used feature extraction methods include two-dimensional Linear Discriminant Analysis (2D-LDA) @cite_26 @cite_33 , two-dimensional Principle Component Analysis (2D-PCA) @cite_13 , Discrete Wavelet Transform (DWT) @cite_12 @cite_26 @cite_41 , Gabor based methods @cite_21 @cite_4 and wavelets-based techniques @cite_36 @cite_10 . In 2D-LDA method, the two-dimensional image matrix is exploited to form scatter matrices between the classes and within the class @cite_26 . 2D-LDA method can be applied for facial features extraction alone or accompanied with the other feature extraction method, as DWT @cite_26 @cite_33 . In 2D-PCA feature extraction method, the covariance matrix representation of the image is derived directly from the original image @cite_26 @cite_13 . The size of the derived principle component matrix is smaller than the original image size that allows to decrease the amount of processing data, and consequently, reduce the required computational memory @cite_40 . However, 2D-LDA and 2D-PCA methods applied in template matching techniques require an additional processing of the image, dimensionality reduction techniques or application of another feature extraction method to achieve higher recognition accuracy, which leads to the increase in processing time.
@cite_26 @cite_4 @cite_33 @cite_41 @cite_36 @cite_10 @cite_21 @cite_40 @cite_13 @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. Abstract Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of texture features in local facial regions and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. Results on the parameter sensitivity experiments demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusion ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth. Facial expression recognition is the most important criteria for effective Human Computer Interaction (HCI) as well as a medium to understand and communicate with children who cannot emote verbally. In this paper, we propose a feature extraction technique by embedding 2D-LDA and 2D-PCA. The features extracted were then tested on standard classifiers i.e., Support Vector Machine (SVM) and K-Nearest Neighbourhood (KNN) classifiers. Facial expression images from JAFFE and Cohn-Kennedy databases were utilized for training as well as testing. Very high facial emotion recognition rate of 97.63 and 94.8 has been obtained with the proposed method for JAFFE and Cohn-Kanade databases respectively. Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10 . Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient. 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. Most of the reported algorithms for facial expression recognition (FER) are based on visible expression databases. However, visible images are affected by illumination variations which can cause significant disparities in image appearance and texture. In this paper preliminary results of a new FER algorithm, using Gauss-Laguerre (GL) filter of circular harmonic wavelets to extract features for infrared images, are presented. By using GL filters with properly tuned-parameters, it is possible to generate a set of redundant wavelets that enable an accurate extraction of complex texture features from an infrared image. In addition, we utilize GL filters that are highly suitable for FER in visible images. The combination of infrared and visible FER using common feature extraction approach saves time and reduces the complexity in a multiple-sensors scenario. K-nearest neighbor is used for classification on OTCBVS and USTC-NVIE databases. The results show effective performance for using GL filters in FER for infrared images. Abstract This paper mainly focuses on dimensional reduction of fused dataset of holistic and geometrical face features vectors by solving singularity problem of linear discriminant analysis and maximizing the Fisher ratio in nonlinear subspace region with the preservation of local discriminative features. The combinational feature vector space is projected into low dimensional subspace using proposed Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis (KLSWFDA) method. Matching score level fusion technique has been applied on projected subspace and combinational entire Gabor subspace is framed. Euclidean distance metric (L2) and support vector machine (SVM) classifier has been implemented to recognize and classify the expressions. Performance of proposed approach is evaluated and compared with state of art approaches. Experimental results on JAFFE, YALE and FD expression database demonstrate the effectiveness of the proposed approach. Even if, most of 2D face recognition approaches reached recognition rate more than 90 in controlled environment, current days face recognition systems degrade their performance in case of uncontrolled environment which includes pose variations, illumination variations, expression variations and ageing effect etc. Inclusion of 3D face analysis gives an age over 2D face recognition as they give vital informations such as 3D shape, texture and depth which improve discrimination power of an algorithm. In this paper, we have investigated different 3D face recognition approaches that are robust to changes in facial expressions and illumination variations. 2D-PCA and 2D-LDA approaches have been extended to 3D face recognition because they can directly work on 2D depth image matrices rather than 1D vectors without need for transformations before feature extraction. In turn, this reduces storage space and time required for computations. 2D depth image is extracted from 3D face model and nose region from depth mapped image has been detected as a reference point for cropping stage to convert model into a standard size. Two Dimensional Principal Component Analysis (2D-PCA) and Two Dimensional Linear Discriminant analysis (2D-LDA) are employed to obtain feature vectors globally compared to feature vectors obtained locally using PCA or LDA. Finally, euclidean distance classifier is applied for comparison of extracted features. A set of experiments on GavabDB 3D face database, which has 61 individuals in total, demonstrated that 3D face recognition using 2D-LDA method has achieved recognition accuracy of 93.3 and EER of 8.96 over database, which is higher compared to 2D-PCA. So, more optimized performance has been achieved using 2D-LDA for 3D face recognition analysis. Face recognition is an extensively researched topic by researchers from diverse disciplines. Several unsupervised statistical feature extraction methods have been used in face recognition, out of these in this paper a comparison of the PCA(eigenfaces) and 2D-PCA approaches on Indian Faces has been presented. To test and compare their performances a series of experiments were performed on ORL database, Yale face database and then on an in-house dataset which has been collected over a span of 6 months. The performance parameters compared here are recognition rate and speed with varying number of training images. The application of various preprocessing techniques which can be used to improve their performance has also been studied. 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.