Human Face Expression Recognition Based on Feature Fusion

2014 ◽  
Vol 536-537 ◽  
pp. 115-120
Author(s):  
Ting Gong ◽  
Yu Biao Liu
Keyword(s):  
Feature Fusion ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Human Face ◽  
Face Expression ◽  
Geometry Feature ◽  
Feature Based ◽  
Face Expression Recognition

The Gabor wavelet is the important technique widely used in the areas of images recognition such as human face expression, it extract the more important grain features for face expression effective, but it does not take into account the relative changes in the important characteristics of each location of the point features. Aiming at recognizing the information of human face expression, fuse the geometry feature based on angle changes at key parts on face expression, and then a radial basis function (RBF) neural network is designed as the classifier to perform recognition. The results of the experiment in the human face expression database indicate that the recognition rate by the feature fusion is obviously superior to that of traditional method.

FEATURE-BASED FACIAL EXPRESSION RECOGNITION: SENSITIVITY ANALYSIS AND EXPERIMENTS WITH A MULTILAYER PERCEPTRON

1999 ◽  
Vol 13 (06) ◽  
pp. 893-911 ◽  
Author(s):  
ZHENGYOU ZHANG
Keyword(s):  
Sensitivity Analysis ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Low Frequency ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Coefficients ◽  
Feature Based

In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducial points on a face, and a set of multiscale and multiorientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e. the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of facial expressions. Then, we have investigated the importance of each individual fiducial point to facial expression recognition. Sensitivity analysis reveals that points on cheeks and on forehead carry little useful information. After discarding them, not only the computational efficiency increases, but also the generalization performance slightly improves. Finally, we have studied the significance of image scales. Experiments show that facial expression recognition is mainly a low frequency process, and a spatial resolution of 64 pixels × 64 pixels is probably enough.

Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition

2014 ◽  
Vol 721 ◽  
pp. 766-770
Author(s):  
Wei Yu Gong ◽  
Fang Xia Lu
Keyword(s):  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Gabor Wavelet ◽  
Projection Algorithm ◽  
Expression Recognition ◽  
Face Expression ◽  
Locality Preserving ◽  
Expression Category ◽  
Face Expression Recognition ◽  
Discriminant Projection

For the problem of features extraction and dimensionality reduction of expression recognition, the paper proposes Gabor Locality Preserving Discriminant Projection (GLPDP) algorithm, which is based on Gabor Wavelet. Firstly, we use Gabor wavelet transform to have an expression feature extraction. Secondly, we improved the locality preserving projection (LPP) algorithm, introducing scatter difference in the LPP objective function to increase divergence constraints among the sample classes and extracts more discriminated features while having the dimensionality reduction. Finally, we use the nearest neighbor classifier to have a classification for expression category. The effectiveness of the proposed methods is validated through the experimental results on JAFFE and Cohn-Kanade Facial expression databases.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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