光流估计下的移动端实时人脸检测

为了提高移动设备人脸检测准确率，提出一种应用于移动设备的实时人脸检测算法。通过改进Viola-Jones方法进行人脸区域快速分割，在不损失速度的情况下提高分割精度；同时应用了光流估计方法将卷积神经网络子网络在离散关键帧上的特征提取结果传播至非关键帧，提高神经网络实际检测运行效率。实验使用YouTube视频人脸数据库、自建20人各1 min正位人脸视频数据库和实际检测项目在不同分辨率下进行，实验结果表明运行速度在2.35帧/秒~22.25帧/秒，达到了一般人脸检测水平；人脸检测在10%误检率下召回率由Viola-Jones的65.93%提高到82.5%~90.8%，接近卷积神经网络检测精度，满足了移动设备实时人脸检测的速度和精度要求。

Real-time face detection for mobile devices with optical flow estimation

To improve the face detection accuracy of mobile devices, a new real-time face detection algorithm for mobile devices was proposed. The improved Viola-Jones was used for a quick region segmentation to improve segmentation precision without decreasing segmentation speed. At the same time, the optical flow estimation method was used to propagate the features of discrete keyframes extracted by the sub-network of a convolution neural network to other non-keyframes, which increased the efficiency of convolution neural network. Experiments were conducted on YouTube video face database, a self-built one-minute face video database of 20 people and the real test items at different resolutions. The results show that the running speed is between 2.35 frames per second and 22.25 frames per second, reaching the average face detection level; the recall rate of face detection is increased from 65.93% to 82.5%-90.8% at rate of 10% false alarm, approaching the detection accuracy of convolution neural network, which satisfies the speed and accuracy requirements for real-time face detection of mobile devices.