Anchor Cascade for Efficient Face Detection

Abstract

Face detection is essential to facial analysis tasks such as facial reenactment and face recognition. Both cascade face detectors and anchor-based face detectors have translated shining demos into practice and received intensive attention from the community. However, cascade face detectors often suffer from a low detection accuracy, while anchor-based face detectors rely heavily on very large networks pre-trained on large scale image classification datasets such as ImageNet [1], which is not computationally efficient for both training and deployment. In this paper, we devise an efficient anchor-based cascade framework called anchor cascade. To improve the detection accuracy by exploring contextual information, we further propose a context pyramid maxout mechanism for anchor cascade. As a result, anchor cascade can train very efficient face detection models with a high detection accuracy. Specifically, comparing with a popular CNN-based cascade face detector MTCNN [2], our anchor cascade face detector greatly improves the detection accuracy, e.g., from 0.9435 to 0.9704 at 1k false positives on FDDB, while it still runs in comparable speed. Experimental results on two widely used face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed framework.

References in corpus (13)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Fully Convolutional Networks for Semantic Segmentation
• UnitBox: An Advanced Object Detection Network
• Going Deeper with Contextual CNN for Hyperspectral Image Classification
• A Fast and Accurate Unconstrained Face Detector
• Face Detection through Scale-Friendly Deep Convolutional Networks
• Scale-Aware Face Detection
• CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
• Faceness-Net: Face Detection through Deep Facial Part Responses

Cited by in corpus (2)

• EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
• Recent Advances in Deep Learning for Object Detection