基于深度学习检测器的多角度人脸关键点检测

针对人脸关键点检测(人脸对齐)在应用场景下的速度和精度需求,首先在SSD基础之上融合更多分布均匀的特征层,对人脸框坐标进行级联预测,形成对于多尺度人脸信息均具有更加鲁棒响应的深度学习检测器MR-SSD。其次在局部二值特征LBF的级联形状回归方法基础上,提出了基于面部像素差值的多角度初始化算法。采用端正人脸正负90°倾斜范围内的五组特征点形状进行初始化,求取每组回归后形状的眼部特征点像素均方差值并以最大者对应方案作为最终回归形状,从而实现对多角度倾斜人脸优异的拟合效果。本文所提出的最优架构可以实时获得极具鲁棒性的人脸框坐标并且可实现对于多角度倾斜人脸的关键点检测。

Multi-angle key point detection of face based on deep learning detector

In order to meet the speed and accuracy requirements of face key point detection(face alignment)in application scenarios,firstly,cascaded prediction is carried out on the basis of SSD(single shot multibox detector),which combines more uniformly distributed feature layers to form MR-SSD(more robust SSD),a deep learning detector with more robust response to multi-scale faces.Secondly,based on the cascade shape regression method of local binary feature(LBF),a multi-angle initialization algorithm based on the difference between the facial pixels is proposed.Five groups of feature points in the 90 degree inclination range of positive and negative face are initialized to achieve excellent fitting effect for inclined face under multi angles.The mean square deviation of each group of feature points after regression is calculated and the maximum corresponding shape is used as the final regression shape.The optimal architecture proposed in this paper can obtain robust face bounding box and face alignment schemes against multi-angle tilt in real time.