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Facial Expression-Aware Face Frontalization
"... Abstract. Face frontalization is a rising technique for view-invariant face analysis. It enables a non-frontal facial image to recover its general facial appearances to frontal view. A few pioneering works have been proposed very recently. However, face frontalization with detailed facial expressio ..."
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Abstract. Face frontalization is a rising technique for view-invariant face analysis. It enables a non-frontal facial image to recover its general facial appearances to frontal view. A few pioneering works have been proposed very recently. However, face frontalization with detailed facial expression recovering is still very challenging due to the non-linear relationships between head-pose and expression variations. In this paper, we propose a novel facial expression-aware face frontalization method aiming at reconstructing the frontal view while maintaining vivid appearances with regards to facial expressions. First of all, we design multiple face shape models as the reference templates in order to fit in with various shape of facial expressions. Each template describes a set of typical facial actions referred to Facial Action Coding System (FACS). Then a template matching strategy is applied by measuring a weighted Chi Square error such that the input image can be matched with the most approximate template. Finally, Robust Statistical face Frontalization (RSF) method is employed for the task of frontal view recovery. This method is validated on a spontaneous facial expression database and the experimental results show that the proposed method outperforms the state-of-the-art methods.
Multi-conditional Latent Variable Model for Joint Facial Action Unit Detection
"... We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detec-tion of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such a ..."
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We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detec-tion of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. This leads to supe-rior performance compared to existing classifiers for the target task that exploit either the discriminative or gener-ative property, but not both. The model learning is per-formed via an efficient, newly proposed Bayesian learning strategy based on Monte Carlo sampling. Consequently, the learned model is robust to data overfitting, regardless of the number of both input features and jointly estimated facial action units. Extensive qualitative and quantitative experimental evaluations are performed on three publicly available datasets (CK+, Shoulder-pain and DISFA). We show that the proposed model outperforms the state-of-the-art methods for the target task on (i) feature fusion, and (ii) multiple facial action unit detection. 1.
