Facial Recognition Based on Discrete Wavelet Transform and Component Analysis Support Vector Machine

In order to realize facial recognition with different characters such as illumination, posture and noise and improve the recognition precision, a facial recognition method based on discrete wavelet transform and least squares support vector machine is proposed. the discrete wavelet transform is used to compress the facial figure and reducing the noise to get the character information component with low frequency, and then the fast independent component analysis is used to obtain the facial character information with low frequency to reduce the dimension further. Finally, the radius basis function is used as the kernel function, and the training data is input to the least squares support vector machine to get the final recognition model. The simulation experiment is simulated in ORL database with Matlab tool, and the result shows the method in this paper can realize the facial recognition.