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.

Van Genuchten equation is the most commonly used soil water characteristic curve equation, and its parameter value precision is the key to the use of the equation. In order to solve these parameters accurately, the Harmony Search (HS) algorithm was introduced, and a new HS algorithm based on the current global information named IGHS was proposed. IGHS algorithm has the following characteristics: firstly, IGHS employs a new method for generating new solution vectors, which uses the current global optimum in the harmony memory. Secondly, in order to avoid premature and enhance global search ability, IGHS disturbs the current global optimum at a certain probability. Lastly, the algorithm is simple, and easy to implement. The experimental results show that the solution accuracy of IGHS is similar to the random Particle Swarm Optimization (PSO) algorithm, but the convergence of IGHS is faster than PSO and the calculated amount is smaller, so IGHS can be used as a new method to calculate Van Genuchten equation parameters.