Recognizing facial actions using Gabor wavelets with neutral face average difference

This work describes a new pre-processing step to classify facial expression. Previous works suggest that Gabor wavelets applied to recognize facial expression images subtracted from neutral face from the same subject could achieve good recognition rate under controlled condition as eye and month alignment. We propose a recognition system where the Gabor kernels are applied on facial expression subtracted from an averaged neutral face. A fast pre-processing technique that generates a small dimension output data is also proposed. A correct recognition rate of 86.6% is obtained in a 7 upper face actions and 81.6% in a 7 lower face actions detection problem using a neural network based classifier. The performance is evaluated in a heterogeneous subject database with head motion and lighting variations.