The illumination changes and environmental noise cause facial recognition accuracy will be reduced

in order to effectively solve this problem

need to be multidimensional variable light color face image automatic identification. But with the current method for automatic face image recognition

slow convergence speed

easy to fall into local minimum

the existence question of error identification. For this

put forward a based on particle swarm optimization neural network of variable light color multidimensional automatic face image recognition method. The method using wavelet transform to obtain first face image of low frequency component

using two-dimensional differential analysis (2 dlda) algorithm to extract the face image linear differential characteristics of low frequency component

using particle swarm optimization and BP neural network for classification and recognition and in ORL face database verify the feasibility of the proposed method. Experimental simulation show that the proposed method the identification accuracy is higher

effectively reduces the influence of uneven illumination in face recognition.