计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100290-5.doi: 10.11896/jsjkx.211100290
付博闻1, 李闯闯1, 梁爱华2
FU Bo-wen1, LI Chuang-chuang1, LIANG Ai-hua2
摘要： 人脸关键点检测作为人脸识别的重要环节,一直是计算机视觉领域的研究热点。为了满足高效轻量级的人脸关键点检测需求,提出了一种基于改进YOLOv4-tiny的人脸关键点快速检测算法。模型输入采用608*608*3的彩色图像,使用CSPDarknet53-tiny网络对输入图像进行主干特征提取,对提取到的特征进行上采样和特征融合,在特征融合之前添加注意力机制来提高检测准确度,同时对YOLOv4-tiny网络的损失函数进行调整,添加人脸关键点的损失计算,实现在人脸目标检测的同时对关键点进行标定定位。模型输出包括人脸标记框和人脸5个关键点。实验结果表明,相比其他网络的人脸关键点检测方法,所提模型在保证识别准确度的基础上,具有更高的识别效率和更低的配置要求,可以满足快速实时检测的需求,且更易部署在边缘设备或者移动设备上。
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