融合多尺度特征的轻量级人脸检测算法

Lightweight Face Detection Algorithm with Multi-scale Feature Fusion

受到移动设备计算能力和存储资源受限的局限,设计高效、高精度的人脸检测器是一个开放性的挑战.因此,文中提出融合多尺度特征的轻量级人脸检测算法(Lightweight Face Detection Algorithm with Multi-scale Feature Fusion,LFDMF),摒弃被视为人脸检测核心组件的多级检测结构.首先,利用现有的轻量级主干特征提取网络编码输入图像.然后,利用提出的颈部网络扩张特征图感受野,并将含有不同感受野的多尺度信息融至单级特征图中.最后,利用提出的多任务敏感检测头对该单级特征图进行人脸分类、回归和关键点检测.相比分而治之的人脸检测器,LFDMF精度更高、计算量更少.LFDMF按模型计算量高低可构建3个不同大小的网络,大模型LFDMF-L在Wider Face数据集上性能较优,中等模型LFDMF-M和小模型LFDMF-S以极低的模型参数量和计算量实现可观性能.

Due to the limitations in computing capacity and storage resources of mobile devices,it is still an open challenge to design an efficient and high-precision face detector.In this paper,a lightweight face detection algorithm with multi-scale feature fusion(LFDMF)is proposed.The multi-level detection structure,regarded as the core component of face detection,is removed.Firstly,the existing lightweight backbone feature extraction network is introduced to encode the input image.Then,the proposed neck network is utilized to expand the receptive field of the feature map,and the multi-scale information with different receptive fields is fused into the one-level feature map.Finally,the proposed multi-task sensitive detector head is employed to perform face classification,regression and key point detection for the one-level feature map.Compared with the face detectors with RetinaFace and DSFD,LFDMF achieves higher accuracy and less computation burden.LFDMF builds three networks of different sizes.The large model,LFDMF-L,is built to achieve the most advanced performance on the Wider Face dataset,while the medium model,LFDMF-M,and the small model,LFDMF-S,achieve impressive performance with a small number of model parameters and less computation.