Face Detection using Deep Learning: An Improved Faster RCNN Approach

2017/01/28 by Xudong Sun, Pengcheng Wu, Sun, Xudong +3 · 2 citations
Computer Science · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis

paper · pdf · doi:10.48550/arxiv.1701.08289

openalex publication_date 2017/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Face Detection with the Faster R-CNN
• Bootstrapping Face Detection with Hard Negative Examples
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Inside-Outside Net: Detecting Objects in Context with Skip Pooling and\n Recurrent Neural Networks
• Robust Real-Time Face Detection

Cited by

• Facial Expression Recognition System Using DNN Accelerator with Multi-threading on FPGA
• EPANet: Efficient Path Aggregation Network for Underwater Fish Detection

Related

• From Facial Parts Responses to Face Detection: A Deep Learning Approach
• Towards a Deep Learning Framework for Unconstrained Face Detection
• Bootstrapping Face Detection with Hard Negative Examples
• Face Detection Using Improved Faster RCNN
• Detecting Faces Using Region-based Fully Convolutional Networks