Active Shape Model (ASM) is a statistical model of object shapes that represents a target structure. ASM can guide machine learning algorithms to fit a set of points representing an object (e.g., face) onto an image. This paper presents a lightweight Convolutional Neural Network (CNN) architecture with a loss function regularized by ASM for face alignment and estimating head pose in the wild. The ASM-based regularization term in the loss function would guide the network to learn faster, generalize better, and hence handle challenging examples even with light-weight network architecture. We define multi-tasks in our loss function that are responsible for detecting facial landmark points, as well as estimating face pose. Learning multiple correlated tasks simultaneously builds synergy and improves the performance of individual tasks. Experimental results on challenging datasets show that our proposed ASM regularized loss function achieves competitive performance for facial landmark points detection and pose estimation using a very light-weight CNN architecture.

本文提出了一种轻量级卷积神经网络架构，使用Active Shape Model辅助loss function进行面部对齐和估计头部姿态，该方法在人脸特征点检测和面部姿态估计任务中取得了与MobileNetV2相当甚至更好的表现，并且具有更少的参数和浮点运算。

ASMNet：一个轻量级的用于脸部对齐和姿态估计的深度神经网络