Improvement of Gaussian kernel function for face recognition

Face recognition has been widely used in information security, access control, financial payment, criminal investigation, and other aspects due to its stability, convenience, ease to forge, and other advantages. The Gaussian blur, a fuzzy algorithm, uses the Gaussian distribution to increase the efficiency of face detection. However, the data distribution of the Gaussian blur model is not effective enough for face recognition. In this paper, we improve the one-dimensional Gaussian kernel function to the two-dimensional Gaussian kernel function and use the Gaussian distribution to improve the accuracy of the recognition system. We improve the Model and adopt the Gaussian calculation method. According to the one-dimensional Gaussian function and two-dimensional Gaussian function to carry on the analysis and operation, the conclusion is drawn that the two-dimensional Gaussian function can enhance the efficiency of face recognition when applied to the designed face recognition system. To verify the effectiveness of our method, we compare the proposed method with Gaussian blur on three different datasets. The experimental results show that our method significantly outperforms Gaussian blur. Our analyses illustrate that the recognition degree and range of the improved model are more comprehensive and broader, decreasing the time used for recognition.