Franck Davoine

Facial expression recognition and synthesis based on an appearance model

This article addresses the issue of expressive face modelling using an active appearance model for facial expression recognition and synthesis. We consider the six universal emotional categories namely joy, anger, fear, disgust, sadness and surprise. After a description of the active appearance model (computed with 3 or only one PCA), we address the active appearance model contribution to automatic facial expression recognition. Then we propose a new method for analysis and synthesis allowing, from a single photo, to cancel the facial expression on a given face and to artificially synthesize novel expressions on this same face. In this last framework, we propose two facial expression modelling approaches.

A solution for facial expression representation and recognition

The design of a recognition system requires careful attention to pattern representation and classifier design. Some statistical approaches choose those features, in a d-dimensional initial space, which allow sample vectors belonging to different categories to occupy compact and disjoint regions in a low-dimensional subspace. The effectiveness of the representation subspace is then determined by how well samples from different classes can be separated. In this paper, we propose a feature selection process that sorts the principal components, generated by principal component analysis, in the order of their importance to solve a specific recognition task. This method provides a low-dimensional representation subspace which has been optimized to improve the classification accuracy. We focus on the problem of facial expression recognition to demonstrate this technique. We also propose a decision tree-based classifier that provides a ‘‘coarse-to-fine’’ classification of new samples by successive projections onto more and more precise representation subspaces. Results confirm, first, that the choice of the representation strongly influences the classification results, second that a classifier has to be designed for a specific representation. r 2002 Elsevier Science B.V. All rights reserved.

Appearance factorization based facial expression recognition and synthesis

Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. We address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. We conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.

Automatic Facial Feature Extraction and Facial Expression Recognition

In this paper, we present an automatic algorithm for facial expression recognition. We first propose a method for automatic facial feature extraction, based on the analysis of outputs of local Gabor filters. Such analysis is done using a spatial adaptive triangulation of the magnitude of the filtered images. Then, we propose a classification procedure for facial expression recognition, considering the internal part of registered still faces. Principal Component Analysis allows to represent faces in a low-dimensional space, defined by basis functions that are adapted to training sets of facial expressions. We show how to select the best basis functions for facial expression recognition, providing a good linear discrimination: results prove the robustness of the recognition method.

Performance of an ICA video watermarking scheme using informed techniques

Independent component analysis (ICA) techniques have been recently used in different watermarking schemes. However, performance of an ICA video watermarking scheme in comparison with those using classical domains, such as the discrete Fourier transform (DCT) domain, is still not clear. In this paper, we attempt to fill this gap. Therefore, we propose a video watermarking scheme, using an informed trellis, applied in two transformed domains obtained by using respectively the DCT transform and an ICA coding technique. We show that, for both domains, the scheme offers a good robustness against MPEG-2 compression, as well as an important capacity level. We consider in this paper data hiding in digital TV channels where data are compressed using MPEG-2.

An Effective EM Algorithm for PCA Mixture Model

This paper studied PCA mixture model in high dimensional space. A novel EM learning approach by using perturbation was proposed for the PCA mixture model. Experiments showed the novel perturbation EM algorithm is more effective in learning PCA mixture model than an existing constrained EM algorithm.