# ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation

arXiv: https://arxiv.org/abs/2103.00119 · HTML: https://mlsanity.com/paper/2103.00119/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors](https://mlsanity.com/paper/1807.00966/) (2018)
- [Deep Alignment Network: A convolutional neural network for robust face alignment](https://mlsanity.com/paper/1706.01789/) (2017)
- [Surpassing Human-Level Face Verification Performance on LFW with GaussianFace](https://mlsanity.com/paper/1404.3840/) (2014)
- [Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment](https://mlsanity.com/paper/1802.06713/) (2018)
- [Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion](https://mlsanity.com/paper/1709.08130/) (2017)
- [Pose-Invariant Face Alignment with a Single CNN](https://mlsanity.com/paper/1707.06286/) (2017)
- [Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation](https://mlsanity.com/paper/1511.07356/) (2015)
- [Pose-Invariant 3D Face Alignment](https://mlsanity.com/paper/1506.03799/) (2015)
- [Face Alignment Assisted by Head Pose Estimation](https://mlsanity.com/paper/1507.03148/) (2015)
- [An Empirical Study of Recent Face Alignment Methods](https://mlsanity.com/paper/1511.05049/) (2015)

## Built on by (most cited first, 10 of 10)

- [Shape Preserving Facial Landmarks with Graph Attention Networks](https://mlsanity.com/paper/2210.07233/) (2022)
- [AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels](https://mlsanity.com/paper/2410.22506/) (2024)
- [ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment](https://mlsanity.com/paper/2203.15835/) (2022)
- [Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks](https://mlsanity.com/paper/2111.07047/) (2021)
- [XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks](https://mlsanity.com/paper/2111.10854/) (2021)
- [Simple Transferability Estimation for Regression Tasks](https://mlsanity.com/paper/2312.00656/) (2023)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss](https://mlsanity.com/paper/2402.01690/) (2024)
- [GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis](https://mlsanity.com/paper/2302.00908/) (2023)
- [U-Net with ResNet Backbone for Garment Landmarking Purpose](https://mlsanity.com/paper/2204.12084/) (2022)

## Abstract (arXiv)

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 a smoother distribution of the facial landmark points. Inspired by transfer learning, during the training process, we gradually harden the regression problem and guide 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 the face pose. Learning multiple correlated tasks simultaneously builds synergy and improves the performance of individual tasks. We compare the performance of our proposed model called ASMNet with MobileNetV2 (which is about 2 times bigger than ASMNet) in both the face alignment and pose estimation tasks. Experimental results on challenging datasets show that by using the proposed ASM assisted loss function, the ASMNet performance is comparable with MobileNetV2 in the face alignment task. In addition, for face pose estimation, ASMNet performs much better than MobileNetV2. 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 compared to many CNN-based models.