Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

Abstract

In order to solve the problem of high dimensionality and low recognition rate caused by complex calculation in face recognition, the author proposes a face recognition algorithm based on weighted DWT and DCT based on particle swarm neural network applied to new energy vehicles. The algorithm first decomposes the face image with wavelet transform, removes the influence of the diagonal component, the weighted low-frequency and high-frequency discrete cosine transform coefficients are extracted as feature vectors, and finally, the particle swarm optimization BP neural network is used for classification and identification. Experimental results show that when the wavelet weights take $a_0=0.9$ , $a_1=0.05$ , and $a_2=0.05$ , the recognition rate reaches the highest. Regardless of whether the low-frequency component continues to increase or decrease, and the high-frequency component continues to decrease or increase, the recognition rate will decrease. When the eigenvector dimension is around 60, the recognition rate difference between the weighted wavelet algorithm and the general low-frequency wavelet algorithm reaches the maximum. The recognition rate of the proposed algorithm is much higher than the other two traditional algorithms. Conclusion. The effectiveness and feasibility of the algorithm are verified on the ORL face database.