Pedestrian and Face Detection with Low Resolution Based on Improved MTCNN

In recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, the large visual variations of pedestrians and faces, such as large pose variations and dark lightings, resulting in lower resolution targets, impose great challenges for these tasks in real-world applications. To solve this problem, we present a conceptually simple, end-to-end, and general framework for pedestrian and face detection. Our approach efficiently detects both pedestrian and face in an image. First, an efficient improved P-Net is developed to detect a pedestrian. Then an efficient improved R-Net1 is developed to filter pedestrian targets in the second level, and improved R-Net2 carries out the preliminary detection of face targets in the remaining pedestrian targets. In order to improve the face detection rate on a small scale, improved R-Net2 introduces a multi-level feature fusion mechanism. Last, an improved O-Net is proposed to identify pedestrian and face regions. Compared to state-of-the-art face detection methods such as Multiscale Cascade CNN、 Faceness、 Two-stage CNN、 MTCNN, the proposed method achieves promising performance on WIDER FACE benchmarks, our method also reaches promising results on the Caltech benchmarks.