Real-time Convolutional Neural Networks for emotion and gender classification
By Octavio Arriaga, Matias Valdenegro-Toro, Paul-Gerhard Plöger. The European Symposium on Artificial Neural Networks, 2017
317 citations recorded by the source corpus.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/53b61d674f7d7758188ed51b81737d545d1641ab
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Soujanya Poria
- Erik Cambria
- Devamanyu Hazarika
- Rada Mihalcea
- Navonil Majumder
- Alexander Gelbukh
- Naoyuki Kubota
- Zhaojie Ju
Citations in the corpus
- Attention mechanism-based CNN for facial expression recognition (2020, background): “The recognition results of [38], [33] and [34] on NCUFE are 91.”
- DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (2018, methodology): “Listener visual features of party i at time t v i,t are extracted using the model introduced by Arriaga, Valdenegro-Toro, and Pl¨oger (2017), pretrained on FER2013 dataset, where feature size D V = 7 .”