A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) pages 1--6, - Apr 2018
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<p> In domains where computational resources and labeled data are limited, such as in robotics, deep networks with millions of weights might not be the optimal solution. In this paper, we introduce a connectivity scheme for pyramidal architectures to increase their capacity for learning features. Experiments on facial expression recognition of unseen people demonstrate that our approach is a potential candidate for applications with restricted resources, due to good generalization performance and low computational cost. We show that our approach generalizes as well as convolutional architectures in this task but uses fewer trainable parameters and is more robust for low-resolution faces. </p>

 

@InProceedings{SBMWW18, 
 	 author =  {Siqueira, Henrique and Barros, Pablo and Magg, Sven and Weber, Cornelius and Wermter, Stefan},  
 	 title = {A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition}, 
 	 booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 	 number = {},
 	 volume = {},
 	 pages = {1--6},
 	 year = {2018},
 	 month = {Apr},
 	 publisher = {},
 	 doi = {}, 
 }