Name
Abstract | ||
|---|---|---|
Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyper-parameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. 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 publicly available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of our proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/WACV.2016.7477450 | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | Field | DocType |
FER,computer vision,engineered features,recognition accuracies,deep neural network architecture,standard face datasets,convolutional layers,max pooling,inception layers,single component architecture,registered facial images,neutral expressions,facial expression databases,MultiPIE,MMI,CK+,DISFA,FERA,SFEW,FER2013,convolutional neural networks,training time,automated facial expression recognition | Expression (mathematics),Computer science,Convolutional neural network,Time delay neural network,Artificial intelligence,Classifier (linguistics),Computer vision,Pattern recognition,Three-dimensional face recognition,Pooling,Feature extraction,Facial expression,Machine learning | Journal |
Volume | ISSN | Citations |
abs/1511.04110 | 2472-6737 | 80 |
PageRank | References | Authors |
1.61 | 34 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ali mollahosseini | 1 | 166 | 5.94 |
| david chan | 2 | 90 | 2.10 |
| Mohammad H. Mahoor | 3 | 861 | 55.59 |