Deep Active Shape Model for Face Alignment and Pose Estimation in Mobile Environment

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 being assisted by ASM for face alignment and estimating head pose in the wild. We use ASM to first guide the network towards learning the smoother distribution of the facial landmark points. Then, during the training process, inspired by the transfer learning, we gradually harden the regression problem and lead the network towards learning the original landmark points distribution. 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. We compare the performance of our proposed CNN, ASMNet with MobileNetV2 (which is about 2 times bigger ASMNet) in both face alignment and pose estimation tasks. Experimental results on challenging datasets show that by using the proposed ASM assisted loss function, ASMNet performance is comparable with MobileNetV2 in face alignment task. Besides, for face pose estimation, ASMNet performs much better than MobileNetV2. Moreover, overall ASMNet achieves an acceptable performance for facial landmark points detection and pose estimation while having a significantly smaller number of parameters and floating-point operations comparing to many CNN-based proposed models.