This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

The traditional salient object detection algorithms are used to apply the underlying features and prior knowledge of the images. Based on conditional random field Markov chain and Adaboost image saliency detection technology, a saliency detection method is proposed to effectively reduce the error caused by the target approaching the edge, which mainly includes the use of absorption Markov chain model to generate the initial saliency map. In this model, the transition probability of each node is defined by the difference of color and texture between each super pixel, and the absorption time of the transition node is calculated as the significant value of each super pixel. A strong classifier optimization model based on Adaboost iterative algorithm is designed.The initial saliency map is processed by the classifier to obtain an optimized saliency map, which highlights the global contrast. In order to extract the saliency region of the final saliency map, a method using conditional random field is designed to segment and extract the saliency region. The results show that the saliency area detected by this method is prominent, the overall contour is clear and has high resolution. At the same time, this method has better performance in accuracy recall curve and histogram.