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Fast facial expression recognition using local binary features and shallow neural networks
Univ Zagreb, Croatia.
Univ Zagreb, Croatia.
Univ Zagreb, Croatia.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6763-5487
2020 (Engelska)Ingår i: The Visual Computer, ISSN 0178-2789, E-ISSN 1432-2315, Vol. 36, nr 1, s. 97-112Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
SPRINGER , 2020. Vol. 36, nr 1, s. 97-112
Nyckelord [en]
Facial expression recognition; Neural networks; Decision tree ensembles; Local binary features
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-164181DOI: 10.1007/s00371-018-1585-8ISI: 000511966800009OAI: oai:DiVA.org:liu-164181DiVA, id: diva2:1413990
Tillgänglig från: 2020-03-11 Skapad: 2020-03-11 Senast uppdaterad: 2020-03-11

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