# FaceBoxes: A CPU Real-time Face Detector with High Accuracy

Source: [FaceBoxes: A CPU Real-time Face Detector with High Accuracy](<https://www.sota2.com/research/paper/68fa65321e209131cea08ce3>)

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

Data updated: 2025-12-24T13:54:00.828Z

Authors: Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

Published: 2017-08-17T12:38:32.000Z

## Abstract

Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales. Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces. As a consequence, the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the number of faces. We comprehensively evaluate this method and present state-of-the-art detection performance on several face detection benchmark datasets, including the AFW, PASCAL face, and FDDB. Code is available at https://github.com/sfzhang15/FaceBoxes

## 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.

- [Face Detection on WIDER FACE 1 (val)](<https://www.sota2.com/research/sota/face-detection-on-wider-face-1-val.md>): Face Detection; WIDER FACE 1 (val); Detection Score (Easy): 76.17.

- [Face Detection on WIDER FACE (val)](<https://www.sota2.com/research/sota/face-detection-on-wider-face-val.md>): Face Detection; WIDER FACE (val); mAP (Easy): 84.

- [Face Detection on FDDB](<https://www.sota2.com/research/sota/face-detection-on-fddb.md>): Face Detection; FDDB; Recall @ 1% FPPI: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1708.05234>)

[PDF](<https://arxiv.org/pdf/1708.05234.pdf>)

[Implementation](<https://github.com/sfzhang15/FaceBoxes>)