Abstract: Aiming at the problem that traditional face detection methods used space vectors to identify high-dimensional face features in complex environments, which had low detection efficiency and poor detection accuracy, the author proposed a face detection method based on dimensionality reduction algorithm of random Fourier supervised feature transformation. Firstly, large-scale multi-dimensional candidate sets were randomly formed by random Fourier mapping, and the best subset of feature sets was obtained by feature selection algorithm. Secondly, based on the extreme learning machine of l_2,1 norm, the random mapping of Gauss kernel fitting effect was generated, the worthless and redundant features of face random
 features were filtered by l_2,1 normalization, and the process was optimized to improve the accuracy of face feature dimensionality reduction. Finally, the dimensionality reduction feature based on dimensionality reduction feature and Adaboost’s face detection algorithm was adopted, and the cascade classifier was obtained by Boosted cascade algorithm to realize the accurate detection of face features. The experimental results show that the missing detection rate and false detection rate of the proposed method are both 8%, and the average detection time is 118 ms. The proposed method has high operational efficiency and detection accuracy.

Key words: random Fourier, supervised, feature transformation, dimensionality reduction algorithm, normalization, face detection