Face Detection using Deep Learning: An Improved Faster RCNN Approach
arXiv:1701.08289
Abstract
In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark.
References in corpus (5)
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
- Bootstrapping Face Detection with Hard Negative Examples
- Face Detection with the Faster R-CNN
- Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Cited by in corpus (11)
- Object Detection with Deep Learning: A Review
- Feature Agglomeration Networks for Single Stage Face Detection
- Seeing Small Faces from Robust Anchor's Perspective
- Unsupervised Hard Example Mining from Videos for Improved Object Detection
- Recurrent Scale Approximation for Object Detection in CNN
- Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
- Multi-Branch Fully Convolutional Network for Face Detection
- Quality Classified Image Analysis with Application to Face Detection and Recognition
- Survey of Face Detection on Low-quality Images
- Nonlinear Collaborative Scheme for Deep Neural Networks
- FA-RPN: Floating Region Proposals for Face Detection