Fast facial expression recognition using local binary features and shallow neural networks
| Autor: | Jörgen Ahlberg, Igor S. Pandzic, Martina Manhart, Ivan Gogić |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Facial expression
Landmark Artificial neural network Computer science business.industry Computer Sciences Feature vector Decision tree Binary number 020207 software engineering Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Computer graphics ComputingMethodologies_PATTERNRECOGNITION Facial expression recognition Neural networks Decision tree ensembles Local binary features Datavetenskap (datalogi) Discriminative model facial expression recognition neural networks decision tree ensembles local binary features 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Popis: | Facial expression recognition applications demand accurate and fast algorithms that can run in real time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (local binary features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within- and cross-database scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-of-the-art algorithms while achieving an order of magnitude improvement in execution time. |
| Databáze: | OpenAIRE |
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