Tianhong Fang

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In this paper, we focus on discrete expression classification using dynamic 3D sequences (4D data) recording the facial movements. A robust approach for registering 4D data is proposed and a variant of local binary patterns on three orthogonal planes is used for feature extraction. We present a fully automatic facial expression recognition pipeline. The(More)
a r t i c l e i n f o Facial expression analysis has interested many researchers in the past decade due to its potential applications in various fields such as human–computer interaction, psychological studies, and facial animation. Three-dimensional facial data has been proven to be insensitive to illumination condition and head pose, and has hence(More)
This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial expression(More)
In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is(More)
We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being “discriminative” or “nondiscriminative” for a given classification task. To(More)
Performance boosts in face recognition have been facilitated by the formation of facial databases, with collection protocols customized to address challenges such as light variability, expressions, pose, sensor/modality differences, and, more recently, uncontrolled acquisition conditions. In this paper, we present database UHDB11, to facilitate 3D-2D face(More)
We present a semi-automatic 3D Facial Expression Recognition system based on geometric facial information. In this approach, the 3D facial meshes are first fitted to an Annotated Face Model (AFM). Then, the Expressive Maps are computed, which indicate the parts of the face that are most expressive according to a particular geometric feature (e.g., vertex(More)
There are many methods available for printer identification of questioned documents, however most of them need identical contents of the training and testing documents. There is no effective method yet when the contents of the training and testing documents are different. To overcome this obstacle a method based on synthetic texture analysis is proposed in(More)
Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions,(More)
Firstly, this paper analyzes the origin of low carbon city and find that low carbon economy and low carbon society have been put forward from the viewpoint of global action and national level, low carbon city is space carrier of low carbon idea and new practice of low carbon economy and low carbon society. Based on the analysis of the literature, the(More)