To solve the problem of quickly and accurately extracting face content in multi-face scenes, a multi-face foreground extraction method based on skin color learning was proposed. Firstly, a skin color foreground segmentation model based on skin color learning was given. According to the results of the papers of skin color experts, 1 200 faces of the famous SPA database were collected for skin color sampling. The learning model was established to obtain the skin color parameters of each race in the color space. The skin color image was segmented according to the parameters to obtain the skin color foreground. Secondly, the face seed area was segmented by using face feature point learning algorithm and skin color foreground information and with 68 common feature points of the face as the target. And the centers of the faces were calculated to construct the elliptical boundary model of the faces and determine the genetic range. Finally, an effective extraction algorithm was established, and the genetic mechanism was used within the elliptical boundaries of the faces to regenerate the faces, so that the effective face areas were extracted. Based on three different databases, 100 representative multi-face images were collected. Experimental results show that the accuracy of the multi-face extraction results of the proposed method is up to 98.4%, and the proposed method has a significant effect on the face content extraction of medium-density crowds as well as provides a basis for the accuracy and usability of the face recognition algorithm.