A Method of Small Face Detection Based on CNN

Under the existing technology, due to the limitation of some scenes, image data will have illumination changes, blurring, occlusion, low resolution and other issues. These problems have brought great challenges to face detection. At present, many algorithm models can recognize face detection well under the condition of positive and high resolution. However, most of the faces in real scenes are lateral and have low resolution. For this kind of face detection, the existing algorithm models will face the problems of accuracy and real-time performance. In this paper, various models of face detection algorithms are deeply studied and analyzed. Combined with the accuracy and speed of the algorithm model, this paper designs a face detection algorithm model based on MTCNN (Multi-task Convolution Neural Network) network model. The algorithm is tested on the WiderFace. WiderFace is the most commonly used dataset in the field of face detection. The result shows that the algorithm is superior to other algorithms in the accuracy and speed of face detection.