Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2103.00119
