Fast Facial Expression Recognition using Local Binary Features and Shallow Neural Networks

Ivan Gogić, Martina Manhart, Igor Pandžić, Jörgen Ahlberg

Abstract:

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.