Abstract
Aiming at the problems that the traditional AdaBoost algorithm has complex feature computation, long training time and low detection rate, a method of face detection based on chaos genetic algorithm optimization adaBoost algorithm was proposed. Firstly, this algorithm uses the image color segmentation for coarse screening on the face image, in order to determine the human skin area. Secondly, the adaptive median filtering is applied to denoise the face image to improve the quality of the face image. Finally, the chaotic genetic algorithm is used to optimize the AdaBoost algorithm to achieve higher detection rate and detection speed. Compared with the traditional AdaBoost algorithm, the experimental results showed that the face detection method based on chaos genetic algorithm optimization AdaBoost algorithm proposed in this paper has a significant improvement in detection rate and detection speed.
Highlights
Face detection uses a certain strategy or a set of image sequences to search for any given image to determine the position and region of the face, to detect the presence of a face in a variety of different images or image sequences, and to determine the number of faces and the process of spatial distribution [1]
You can get it from table 1, this algorithm is compared with the traditional AdaBoost algorithm and the AdaBoost algorithm based on skin segmentation, the average number of features required in the training process is much less, the training time is reduced a lot
A method of face detection based on chaos genetic algorithm optimization adaBoost algorithm is proposed
Summary
Face detection uses a certain strategy or a set of image sequences to search for any given image to determine the position and region of the face, to detect the presence of a face in a variety of different images or image sequences, and to determine the number of faces and the process of spatial distribution [1]. Face detection is a key technology of face information processing which has a very broad application [2], such as: face recognition; face tracking; pose estimation; facial expression recognition; image retrieval and digital video etc [3]. Face detection methods are mainly based on the feature detection method and statistical based detection method [4]. The feature-based detection method is intuitive and easy to implement, but there are great limitations. The statistical detection method is not easy to implement, but it has better robustness and universality [5]. The AdaBoost algorithm based on statistical detection method is used as the core algorithm of face detection. The skin color segmentation is used to screen the face image.
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