Skip to content
Cross-Database Facial Expression Recognition Based on Fine-Tuned Deep Convolutional Network
Python
Branch: master
Clone or download
Latest commit b558904 Apr 17, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
images Add files via upload Apr 17, 2018
models Delete samples Apr 17, 2018
7Subjects7Expressions.png Add files via upload Oct 10, 2017
README.md Update README.md Oct 11, 2017
conference_poster_6-1.png Add files via upload Oct 10, 2017

README.md

Cross-Database Facial Expression Recognition Based on Fine-Tuned Deep Convolutional Network

Follow the link to access full paper on SIBGRAPI library.

Abstract

ExpressionsImages Facial expression recognition is a very important research field to understand human emotions. Many facial expression recognition systems have been proposed in the literature over the years. Some of these methods use neural network approaches with deep architectures to address the problem. Although it seems that the facial expression recognition problem has been solved, there is a large difference between the results achieved using the same database to train and test the network and the cross-database protocol. In this paper, we extensively investigate the performance influence of fine-tuning with cross-database approach. In order to perform the study, the VGG-Face Deep Convolutional Network model (pre-trained for face recognition) was fine-tuned to recognize facial expressions considering different well-established databases in the literature: CK+, JAFFE, MMI, RaFD, KDEF, BU3DFE, and AR Face. The cross-database experiments were organized so that one of the databases was separated as test set and the others as training, and each experiment was ran multiple times to ensure the results. Our results show a significant improvement on the use of pre-trained models against randomly initialized Convolutional Neural Networks on the facial expression recognition problem, for example achieving 88.58%, 67.03%, 85.97%, and 72.55% average accuracy testing in the CK+, MMI, RaFD, and KDEF, respectively. Additionally, in absolute terms, the results show an improvement in the literature for cross-database facial expression recognition with the use of pre-trained models.

Models and Codes

The models of the best result in each of five groups on VGG-FineTuning and VGG-Random can be found here.

Additional information

More informations about this paper at LCAD wiki

Poster

PosterImage

You can’t perform that action at this time.