A Fast Face Detection Method via Convolutional Neural Network

Abstract

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection.

11 figures, 30 pages, To appear in Neurocomputing

References in corpus (7)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Two-Stream Convolutional Networks for Action Recognition in Videos
• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
• DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
• Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
• Object Discovery via Cohesion Measurement
• Sample-Optimal Average-Case Sparse Fourier Transform in Two Dimensions