Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network

Abstract

This paper presents a new method to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI) was used to extract features from the global information of the images and then Radial Basis Function (RBF) Network was employed to classify the facial expressions, based on the features which had been extracted by PZMI. Also, the images were preprocessed to enhance their gray-level, which helps to increase the accuracy of classification. For JAFFE facial expression database, the achieved rate of classification in our experiment is 98.33%. This result leads to a conclusion that the proposed method can ensure a high accuracy rate of classification. Index Terms-Facial expression classification, pseudoZernike moment invariant, RBF neural network.

Final expression classification based on pseaudo zernike moment invariant, zernike moment invariant, principal component analysis and radial basis function neural network

“…Our obtained classification rate is 98.76% (Table 1). To compare the effectiveness of the proposed method with the Single Feature Neural Network (SFNN) human face expression classification systems, we have developed the SFNN systems using the PZMI+RBF [18], ZMI+RBF [17] and PCA+RBF [19]. For these systems, we have selected the PZMI as feature domains with order 9 and 10 which have 40 elements; the ZMI with order 9 and 10 which include 21 feature elements and finally the PCA with 30 largest values.…”

Wavelet based approach for facial expression recognition

“…For instance, a smile expresses hospitality and affection, a lift of eyebrows shows confusion, a wince of forehead portrays fear and anxiety. According to Ekman [1], humans have universal facial expressions and the expressions categorized into six classes these are happiness, sadness, disgust, anger, surprise, and fear.…”

“…Several methods for facial expression recognition have been developed and implemented. Long et al [1] examined facial expression recognition using Pseudo Zernike Moment Invariant (PZMI) as a feature extraction from the global information of images and the Radial Basis Function (RBF) network was employed to be a classifier. In addition, Bashyal and Venayagamoorthy [2] applied Gabor wavelet and learning vector quantization (LVQ) in their study.…”

C12. A Zernike Moment Based Rotational Invariant Watermarking Scheme

“…One of the basic requirements in the design of pattern recognition systems is the selection of the appropriate features to be extracted from the image/object, for the purpose of classification [1][2][3][4][5][6][7][8][9][10]. The automatic digital recognition of a received object/image is based on extracting its features and comparing them with the features of some stored objects/images data box, to choose the one that have the most similar.…”

“…It has been reported in [6,7] that pseudo Zernike moments are less sensitive to noise. PZM can be made scaling and translation invariant after certain geometric transformations [8,9]. In [12] it has also reported that the pseudo Zernike radial polynomials R nm are recursively computed as,…”