针对头部姿态变化较大、脸部遮挡等情况下,由面部特征类型多样和尺度不同造成的面部特征点检测准确度较低的问题,提出了一种面部分组特征线条化和点热图回归相结合的人脸特征点检测方法,并设计了两段式堆叠沙漏网络深度学习模型来实现图像特征分析与特征点定位。利用提出的方法开发了检测算法,并利用该领域几个典型的公共图像数据集,将所提方法与其他方法进行实验对比。结果表明,提出的方法可以适应姿态变化和脸部部分遮挡的应用,相比其他方法,具有检测误差较小、人脸面部特征点检测准确度较高的优势。

A method that combines facial dividing feature line and point heatmap regression is proposed to address the problem of low accuracy of face landmark detection caused by different facial feature types and scales in the cases of large posture changes and occlusion. A deep learning model based on two-stage stacked hourglass network is designed to realize feature analysis and landmark location. Based on the proposed method, the detection algorithm is developed, and the proposed method is compared with other methods by experiments based on several common image datasets. The experimental results show that the proposed method can adapt to the applications of large posture changes and face partial occlusion. Compared with other methods, the proposed method has less detection error and higher accuracy in face landmark detection.