Fast facial expression recognition for emotion awareness disposal

Eckert, Martina; Gil, Almudena; Zapatero, Diego; Meneses, Juan; Martínez-Ortega, José-Fernán

doi:10.1109/icce-berlin.2016.7684749

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…This is normally based on an established two dimensional or three dimensional (2D/3D) facial model to locate feature points based on shape parameters and various derived facial detection models, such as Active Appearance Mode (AAM) [17,18]. Some fast landmark extraction methods have been proposed in recent years by using the combining method with face and eye detection with the Vilas-Jones algorithm and together with a local descriptor to detect the interested region and extract the extreme points as landmarks [19]. Other research extracts the accurate localization of landmarks, relying on the geometrical properties of facial shape with the differential geometry as the theoretical substratum [20].…”

Section: Features For Facial Expression Recognitionmentioning

confidence: 99%

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

et al. 2017

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Abstract:Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and corresponding high-performance classifier selection are still a big challenge. Specific features/classifiers display different performance in different datasets. There has currently been no consensus in the literature that any expression feature or classifier is always good in all cases. Although the recently updated deep learning algorithm, which uses learning deep feature instead of manual construction, appears in the expression recognition research, the limitation of training samples is still an obstacle of practical application. In this paper, we aim to find an effective solution based on a fusion and association learning strategy with typical manual features and classifiers. Taking these typical features and classifiers in facial expression area as a basis, we fully analyse their fusion performance. Meanwhile, to emphasize the major attributions of affective computing, we select facial expression relative Action Units (AUs) as basic components. In addition, we employ association rules to mine the relationships between AUs and facial expressions. Based on a comprehensive analysis from different perspectives, we propose a novel facial expression recognition approach that uses multiple features and multiple classifiers embedded into a stacking framework based on AUs. Extensive experiments on two public datasets show that our proposed multi-layer fusion system based on optimal AUs weighting has gained dramatic improvements on facial expression recognition in comparison to an individual feature/classifier and some state-of-the-art methods, including the recent deep learning based expression recognition one.

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