# FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

arXiv: https://arxiv.org/abs/1609.06591 · HTML: https://mlsanity.com/paper/1609.06591/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

## Built on by (most cited first, 25 of 25)

- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition](https://mlsanity.com/paper/2104.05160/) (2021)
- [Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2106.03487/) (2021)
- [Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher](https://mlsanity.com/paper/2205.14361/) (2022)
- [Spatiotemporal Contrastive Learning of Facial Expressions in Videos](https://mlsanity.com/paper/2108.03064/) (2021)
- [Disentangling Identity and Pose for Facial Expression Recognition](https://mlsanity.com/paper/2208.08106/) (2022)
- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos](https://mlsanity.com/paper/2303.10421/) (2023)
- [Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition](https://mlsanity.com/paper/2406.08997/) (2024)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images](https://mlsanity.com/paper/2104.11274/) (2021)
- [MAFER: a Multi-resolution Approach to Facial Expression Recognition](https://mlsanity.com/paper/2105.02481/) (2021)
- [Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition](https://mlsanity.com/paper/2410.17622/) (2024)
- [Semantic Data Augmentation for Long-tailed Facial Expression Recognition](https://mlsanity.com/paper/2411.17254/) (2024)
- [Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue](https://mlsanity.com/paper/2506.20268/) (2025)
- [On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study](https://mlsanity.com/paper/2104.02653/) (2021)
- [Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal](https://mlsanity.com/paper/2106.01467/) (2021)
- [Cross-Centroid Ripple Pattern for Facial Expression Recognition](https://mlsanity.com/paper/2201.05958/) (2022)
- [RARITYNet: Rarity Guided Affective Emotion Learning Framework](https://mlsanity.com/paper/2205.08595/) (2022)
- [Adaptive Graph-Based Feature Normalization for Facial Expression Recognition](https://mlsanity.com/paper/2207.11123/) (2022)
- [More comprehensive facial inversion for more effective expression recognition](https://mlsanity.com/paper/2211.13564/) (2022)
- [e-Inu: Simulating A Quadruped Robot With Emotional Sentience](https://mlsanity.com/paper/2301.00964/) (2023)
- [Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition](https://mlsanity.com/paper/2303.11840/) (2023)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)

## Abstract (arXiv)

Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redun- dant information from the pre-trained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully- connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.