A Novel Gabor Filter Selection Based on Spectral Difference and Minimum Error Rate for Facial Expression Recognition.
ABSTRACT A new feature selection approach is proposed for facial expression recognition system. The features are extracted using Gabor filters from Grey-scale images for characterizing facial texture. Then, an adaptive filter selection (AFS) algorithm is applied to choose the best subset of Gabor filters with different scales and orientations. In AFS algorithm, the filters are selected based on spectral difference between the original image and the noisy image in Gabor wavelet domain. After that, the optimum subset of filters is selected based on minimum error rate. This subset of Gabor filters is used for feature extraction. The extracted features are classified by adopting a multiple linear discriminant analysis (LDA) classifier. Experiments on different databases are carried out that the method is efficient for facial expression recognition.
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ABSTRACT: In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.IEEE Transactions on Image Processing 02/2007; 16(1):172-87. · 3.20 Impact Factor
Conference Proceeding: Comprehensive Database for Facial Expression Analysis.[show abstract] [hide abstract]
ABSTRACT: Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non -verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression analysis.4th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), 26-30 March 2000, Grenoble, France; 01/2000
Conference Proceeding: Coding facial expressions with Gabor wavelets[show abstract] [hide abstract]
ABSTRACT: A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage. The Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfacesAutomatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on; 05/1998