# Going Deeper in Facial Expression Recognition using Deep Neural Networks

arXiv: https://arxiv.org/abs/1511.04110 · HTML: https://mlsanity.com/paper/1511.04110/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2015.

## Built on by (most cited first, 30 of 43)

- [SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction](https://mlsanity.com/paper/2106.03021/) (2021)
- [Facial Emotion Recognition: State of the Art Performance on FER2013](https://mlsanity.com/paper/2105.03588/) (2021)
- [Learning to Amend Facial Expression Representation via De-albino and Affinity](https://mlsanity.com/paper/2103.10189/) (2021)
- [LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision](https://mlsanity.com/paper/2405.14331/) (2024)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Face Trees for Expression Recognition](https://mlsanity.com/paper/2112.02487/) (2021)
- [MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition](https://mlsanity.com/paper/2209.08988/) (2022)
- [Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU](https://mlsanity.com/paper/2305.15422/) (2023)
- [Identifying the Context Shift between Test Benchmarks and Production Data](https://mlsanity.com/paper/2207.01059/) (2022)
- [Critically examining the Domain Generalizability of Facial Expression Recognition models](https://mlsanity.com/paper/2106.15453/) (2021)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge](https://mlsanity.com/paper/2306.08657/) (2023)
- [Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach](https://mlsanity.com/paper/2311.05567/) (2023)
- [Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation](https://mlsanity.com/paper/2106.04332/) (2021)
- [Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction](https://mlsanity.com/paper/2206.09509/) (2022)
- [Efficient Neural Architecture Search for Emotion Recognition](https://mlsanity.com/paper/2303.13653/) (2023)
- [Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features](https://mlsanity.com/paper/2308.06197/) (2023)
- [ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection](https://mlsanity.com/paper/2412.13174/) (2024)
- [Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images](https://mlsanity.com/paper/2104.11274/) (2021)
- [How far generated data can impact Neural Networks performance?](https://mlsanity.com/paper/2303.15223/) (2023)
- [BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions](https://mlsanity.com/paper/2402.13955/) (2024)
- [Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition](https://mlsanity.com/paper/2501.01816/) (2025)
- [A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition](https://mlsanity.com/paper/2603.12221/) (2026)
- [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)
- [Facial Expressions Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/2107.08640/) (2021)
- [Deep learning for identification and face, gender, expression recognition under constraints](https://mlsanity.com/paper/2111.01930/) (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)

## Abstract (arXiv)

Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem. Despite efforts made in developing various methods for FER, existing approaches traditionally lack generalizability when applied to unseen images or those that are captured in wild setting. Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyperparameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. Nevertheless, the results are not significant when they are applied to novel data. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publically available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks and in both accuracy and training time.