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Small Scale Face Detection Method in Dense Scene

To address the problem of low detection accuracy that still exists for small-scale face detection in dense scenes, this paper presents a face detection algorithm based on neighborhood system theory, which uses existing image processing techniques and logical relationships between neighborhoods. The face neighborhood and the head neighborhood of the human body are used as a neighborhood chain. And based on the correlation between them, a simple and easy-to-implement detection technique is used to extract the features in the different neighborhoods, respectively. The idea of the algorithm is similar to the correlation in big data thinking. The detection method is based on manual experience, easy to use with strong logical rules, avoids the learning process of large samples, and has low performance requirements on experimental equipment. The MATLAB experimental results show that the Precision is 88.8% and the Recall is 90.0%. It proves that the method can have a good accuracy even without relying on machine learning and only relying on simple manual experience.