# Face Detection Using Improved Faster RCNN

Source: [Face Detection Using Improved Faster RCNN](<https://www.sota2.com/research/paper/68fa640c1e209131cea0256b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-14T16:51:36.848Z

Authors: Changzheng Zhang, Xiang Xu, Dandan Tu

Published: 2018-02-06T05:25:02.000Z

## Abstract

Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection. In this report, we propose a detailed designed Faster RCNN method named FDNet1.0 for face detection. Several techniques were employed including multi-scale training, multi-scale testing, light-designed RCNN, some tricks for inference and a vote-based ensemble method. Our method achieves two 1th places and one 2nd place in three tasks over WIDER FACE validation dataset (easy set, medium set, hard set).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1802.02142>)

[PDF](<https://arxiv.org/pdf/1802.02142.pdf>)