# Deep Learning For Smile Recognition

arXiv: https://arxiv.org/abs/1602.00172 · HTML: https://mlsanity.com/paper/1602.00172/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2016.

## Built on by (most cited first, 3 of 3)

- [FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS](https://mlsanity.com/paper/2209.14727/) (2022)
- [An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset](https://mlsanity.com/paper/2304.03064/) (2023)
- [Vulnerability Detection Using Two-Stage Deep Learning Models](https://mlsanity.com/paper/2305.09673/) (2023)

## Abstract (arXiv)

Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.