A convolutional neural network cascade for face detection

• Published in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5325 - 5334
• Main Authors: Haoxiang Li, Zhe Lin, Xiaohui Shen, Brandt, Jonathan, Gang Hua
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2015
• Subjects: Image resolution; Tin
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2015.7299170

In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.