Higher order orthogonal moments for invariant facial expression recognition

Authors:
Seyed Mehdi Lajevardi;Zahir M. Hussain
Affiliations:
School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia;School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia
Venue:
Digital Signal Processing
Year:
2010

Citing 10
Cited 3

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Coding Facial Expressions with Gabor Wavelets

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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A Comparative Study on Region-Based Moments for Facial Expression Recognition

CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Invariant image watermark using Zernike moments

IEEE Transactions on Circuits and Systems for Video Technology

Image representation using separable two-dimensional continuous and discrete orthogonal moments

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Discriminative Zernike and Pseudo Zernike Moments for Face Recognition

International Journal of Computer Vision and Image Processing
Image quality assessment for different wavelet compression techniques in a visual communication framework

Modelling and Simulation in Engineering

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Abstract

Automatic facial expression recognition (FER) is a sub-area of face analysis research that is based heavily on methods of computer vision, machine learning, and image processing. This study proposes a rotation and noise invariant FER system using an orthogonal invariant moment, namely, Zernike moments (ZM) as a feature extractor and Naive Bayesian (NB) classifier. The system is fully automatic and can recognize seven different expressions. Illumination condition, pose, rotation, noise and others changing in the image are challenging task in pattern recognition system. Simulation results on different databases indicated that higher order ZM features are robust in images that are affected by noise and rotation, whereas the computational rate for feature extraction is lower than other methods.