Facial expression recognition based on cortex-like mechanisms

Authors:
Heng Zhao;Xiaoping Wang;Qiang Zhang
Affiliations:
School of Life Sciences and Technology, Xidian University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China;School of Science, Xidian University, Xi'an, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2012

Citing 9
Cited 0

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using mixture covariance matrices to improve face and facial expression recognitions

Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Object Recognition with Features Inspired by Visual Cortex

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields

International Journal of Computer Vision
Facial expression recognition based on shape and texture

Pattern Recognition
Facial expression recognition based on Local Binary Patterns: A comprehensive study

Image and Vision Computing
Facial expression recognition using constructive feedforward neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Facial expression recognition using kernel canonical correlation analysis (KCCA)

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

As an important aspect of human-computer interaction, auto facial expression recognition tries to make a computer extract and analyze human's expression from input face image. In this paper, a facial expression recognition method based on cortex-like mechanisms and visual receptive field is presented. Some biological vision theory such as excited field and inhibitory field are made use to imitate human visual perception mechanism for features extracting. The support vector machine is used for the facial expression classification. As this new feature has very good expression ability for visual perception, the experiments on JAFFE and TFEID databases indicates that the method has the better performance in robustness and accuracy than some present algorithms based on PCA, LDA, LBP and so on.