Higher order orthogonal moments for invariant facial expression recognition

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.