ICA and Gabor representation for facial expression recognition

Abstract

Two hybrid systems for classifying seven categories of human facial expression are proposed. The first system combines independent component analysis (ICA) and support vector machines (SVMs). The original face image database is decomposed into linear combinations of several basis images, where the corresponding coefficients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values. The classification accuracy of this system is compared against that of baseline techniques that combine ICA with either two-class cosine similarity classifiers or twoclass maximum correlation classifiers, when we classify facial expressions into these seven classes. We found that, ICA decomposition combined with SVMs outperforms the aforementioned baseline classifiers. The second system proposed operates in two steps: first, a set of Gabor wavelets (GWs) is applied to the original face image database and, second, the new features obtained are classified by using either SVMs or cosine similarity classifiers or maximum correlation classifier. The best facial expression recognition rate is achieved when Gabor wavelets are combined with SVMs. 1NTRODUCTlONFrom the perspective of psychology and anthropology, the evolution of human brain is reflected by the human language that represents a major milestone in the human evolution. The language alone does not seem to be sufficient for successful social (human-to-human) interaction. Therefore. the evolution o f a nonverbal signaling system, such as the facial expression mechanism has captured an increased attention in psychology and anthropology for a better understanding of the social context [I]. Unlike the human-to-human interaction that takes into consideration the facial expression, human computer interaction systems that use facial expression analysis have been introduced only recently 121, [3], [4] and [51. Reliable facial expression modeling and, particularly, human emotions recognition, are challenging tasks since there is no pure emotion. A particular emotion rather is a complex combination of several facial expressions. Moreover, the emotions can vary in intensity, which makes emotion recognition even more difficult. Basically, there are two types of approaches to cope with facial expression recognition: appearance-based methods and geometric feature-based methods. For the first method the fiducial points ofthe face are selected either manually [b] or automaticallyThis work was supported by the European Union Research Training Network "Multi-modal Human-Computer Interartion (HPRN-CT-2000-001 I I).[7]. The face images are convolved with Gabor filters and the responses extracted from the face images at fiducial points form vectors that are futher used for classification. Alternatively, the Gabor filters can he applied to the entire face image instead to specific face regions. Regarding the geometric feature-based methods, the positions of a set of fiducial points in a face form a feature vector that represents the face geom...

A Coarse-to-Fine Classification Scheme for Facial Expression Recognition

“…Other reports [5][6][7][8][9] on the same database did not give the recognition rate for novel expressers expression.…”

“…Numerous algorithms for facial expression analysis from static images have been proposed [1,2,3] and the Japanese Female Facial Expression (JAFFE) Database is one of the common databases for testing these methods [4][5][6][7][8][9][10].…”

“…Guo and Dyer [7] also adopted a similar face representation and they used linear programming technique to carry out simultaneous feature selection and classifier training. Buciu et al [8] adopted ICA and Gabor representation for facial expression recognition. Neural Networks have been considered in [9,10].…”

Facial Expression Recognition Based on TensorFlow Platform

“…Caifeng Shan et al using SVM classifier based on the LBP features to achieve facial expression recognition [18]. Ioan Buciu et al using SVM classifier based on ICA and Gabor features for facial expression classification, the facial expression recognition system realized by combining Gabor wavelet and SVM classifier can achieve a higher recognition rate [20]. Xia Mao et al [17] realized robust facial expression recognition based on RPCA and AdaBoost.…”

Recognizing facial expressions at low resolution

“…Donato et al [7] explored different techniques to face image representation for facial action recognition, which include holistic spatial analysis, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Local Feature Analysis (LFA), and Linear Discriminant Analysis (LDA); and local schemes such as Gabor-wavelet representation and local principal components; Gabor-wavelet representation and ICA performed best. Recently Buciu et al [14] adopted ICA and Gabor-wavelet representation for facial expression recognition. Although Gabor-wavelet representations have been widely adopted [13,7,15,14,16], it is computationally expensive to convolve face images with multi-banks of Gabor filters to extract multiscale and orientational coefficients.…”