Yunshu Hou

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Michael Spivey

University of California, Merced

Cristina Bicchieri

University of Pennsylvania

Don Ross

University College Cork

Julian Kiverstein

Academic Medical Centre/ Universiteit van Amsterdam

Sonja Eisenbeiss

University of Cologne

David Seamon

Kansas State University

Stephen Yablo

Massachusetts Institute of Technology (MIT)

John Sutton

Macquarie University

Eros Carvalho

Universidade Federal do Rio Grande do Sul

Keith Laws

University of Hertfordshire

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Papers by Yunshu Hou

Robust Object Tracking Based on Uncertainty Factorization Subspace Constraints Optical Flow

Lecture Notes in Computer Science, 2005

ABSTRACT

Robust tracking of nonrigid objects using techniques of inverse component uncertainty factorization subspace constraints optical flow

Multi-Stream Asynchrony Modeling for Audio Visual Speech Recognition

Speech Recognition, 2008

ABSTRACT In this paper, two multi-stream asynchrony Dynamic Bayesian Network models (MS-ADBN mode... more ABSTRACT In this paper, two multi-stream asynchrony Dynamic Bayesian Network models (MS-ADBN model and MM-ADBN model) are proposed for audio-visual speech recognition (AVSR). The proposed models, with different topology structures, loose the asynchrony of audio and visual streams to word level. For MS-ADBN model, both in audio stream and in visual stream, each word is composed of its corresponding phones, and each phone is associated with observation vector. MM- ADBN model is an augmentation of MS-ADBN model, a level of hidden nodes--state level, is added between the phone level and the observation node level, to describe the dynamic process of phones. Essentially, MS-ADBN model is a word model, while MM-ADBN model is a phone model. Speech recognition experiments are done on a digit audio-visual (A-V) database, as well as on a continuous A-V database. The results demonstrate that the asynchrony description between audio and visual stream is important for AVSR system, and MM-ADBN model has the best performance for the task of continuous A-V speech recognition.

3D Face Alignment via Cascade 2D Shape Alignment and Constrained Structure from Motion

Lecture Notes in Computer Science, 2009

In this paper, we consider fitting a 3D wireframe face model to continuous video sequences for th... more In this paper, we consider fitting a 3D wireframe face model to continuous video sequences for the tasks of simultaneous tracking of rigid head motion and non-rigid facial animation. We propose a two-level integrated model for accurate 3D face alignment. At the low level, the 2D shape is accurately extracted via a regularized shape model relied on a cascaded parameter/constraint

DBN Based Models for Audio-Visual Speech Analysis and Recognition

Lecture Notes in Computer Science, 2006

We present an audio-visual automatic speech recognition system, which significantly improves spee... more We present an audio-visual automatic speech recognition system, which significantly improves speech recognition performance over a wide range of acoustic noise levels, as well as under clean audio conditions. The system consists of three components: (i) a visual module, (ii) an acoustic module, and (iii) a Dynamic Bayesian Network-based recognition module. The vision module, locates and tracks the speaker head,

Smooth adaptive fitting of 3D face model for the estimation of rigid and nonrigid facial motion in video sequences

Signal Processing: Image Communication, 2011

We propose a 3D wireframe face model alignment for the task of simultaneously tracking of rigid h... more We propose a 3D wireframe face model alignment for the task of simultaneously tracking of rigid head motion and nonrigid facial expressions in video sequences. The system integrates two levels: (i) at the low level, automatic and accurate location of facial features are obtained via a cascaded optimization algorithm of a 2D shape model, (ii) at the high level, we recover, via minimizing an energy function, the optimal motion parameters of the 3D model, namely the 3D rigid motion parameters and seven nonrigid animation (Action Unit) parameters. In this latter inference, a 3D face shape model (Candide) is automatically fitted to the image sequence via a least squares minimization of the energy, defined as the residual between the projected 3D wireframe model and the 2D shape model, meanwhile imposing temporal and spatial motion-smoothness constraints over the 3D model points. Our proposed system tackles many disadvantages of the optimization and training associated with active appearance models. Extensive fitting and tracking experiments demonstrate the feasibility, accuracy and effectiveness of the developed methods. Qualitative and quantitative performance of the proposed system on several facial sequences, indicate its potential usefulness for multimedia applications, as well as facial expression analysis.

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Robust Shape-Based Head Tracking

by Hichem Sahli and Yunshu Hou

This work presents a new method to automatically locate frontal facial feature points under large... more This work presents a new method to automatically locate frontal facial feature points under large scene variations (illumination, pose and facial expressions). First, we use a kernel-based tracker to detect and track the facial region in an image sequence. Then the results of the face tracking, i.e. face region and face pose, are used to constrain prominent facial feature detection and tracking. In our case, eyes and mouth corners are considered as prominent facial features. In a final step, we propose an improvement to the Bayesian Tangent Shape Model for the detection and tracking of the full shape model. A constrained regularization algorithm is proposed using the head pose and the accurately aligned prominent features to constrain the deformation parameters of the shape model. Extensive experiments demonstrate the accuracy and effectiveness of our proposed method.