Face Detection Algorithm Based on Lightweight Convolutional Neural Network

doi:10.19678/j.issn.1000-3428.0057249

Abstract

Abstract: When deployed to mobile terminals,face detection applications are usually limited by the computing power and storage resources of the devices.To address the problem,an improved face detection algorithm called Lightweight-SSH using lightweight convolutional neural network is proposed. This face detection algorithm is designed based on the Single Stage Headless Face Detector(SSH) algorithm,and employs MobileNet-based lightweight convolutional neural network to extract the features of sample data,reducing the number of parameters and the amount of computation.A deformable convolution layer is introduced into the detection module of the SSH network to improve the ability of the convolutional neural network to model human face deformation.Experimental results on the Wider Face dataset show that,compared with commonly used face detection algorithms,the Lightweight-SSH algorithm significantly reduces model complexity and improves model detection speed while ensuring detection accuracy.

Key words: face detection, deformable convolution, MobileNet, Single Stage Headless Face Detector(SSH), Lightweight-SSH algorithm

摘要： 针对人脸检测在移动端应用时面临的移动设备计算能力及存储资源受限等问题，设计一种基于轻量级卷积神经网络的改进人脸检测算法Lightweight-SSH。基于单点无头人脸检测器（SSH）人脸检测算法，采用基于MobileNet的轻量级卷积神经网络对样本数据进行特征提取，减少模型的参数量和计算量，通过在SSH网络的检测模块中引入可变形卷积层，提升卷积神经网络对人脸形变的建模能力。在Wider Face数据集上的实验结果表明，与常用人脸检测算法相比，Lightweight-SSH算法在保证检测精度的前提下，明显降低模型复杂度，并提高了模型检测速度。

关键词: 人脸检测, 可变形卷积, MobileNet网络, 单点无头人脸检测器, Lightweight-SSH算法

CLC Number:

TP391.41

ZHU Lingling, GAO Chao, CHEN Fucai. Face Detection Algorithm Based on Lightweight Convolutional Neural Network[J]. Computer Engineering, 2021, 47(7): 273-280.

朱灵灵, 高超, 陈福才. 基于轻量级卷积神经网络的人脸检测算法[J]. 计算机工程, 2021, 47(7): 273-280.

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