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Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

Simon Lucey¹, Yang Wang, Mark Cox

¹Robotics Institute, Carnegie Mellon University, Pittsburgh PA 15213, USA.

Image and Vision Computing

|January 5, 2010

Summary

This summary is machine-generated.

This study introduces the exhaustive local search (ELS) algorithm for constrained local models (CLM), significantly improving non-rigid face alignment speed and accuracy over existing methods. ELS achieves real-time performance for robust facial tracking.

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Area of Science:

Computer Vision
Machine Learning
Biometrics

Background:

Active Appearance Models (AAMs) are effective for non-rigid face alignment but face trade-offs between speed and accuracy.
The 'simultaneous' AAM fitting is accurate but slow (2-3 fps), while 'project-out' is fast (>200 fps) but inaccurate.
Constrained Local Models (CLMs) offer a discriminative approach to face registration.

Purpose of the Study:

To introduce an enhanced CLM method for non-rigid face registration and tracking.
To achieve superior performance and real-time fitting speeds compared to existing AAM and CLM algorithms.
To explore theoretical advantages, including parallelization, of the proposed method.

Main Methods:

Proposed an extension to the CLM framework, termed the 'exhaustive local search' (ELS) algorithm.
Employed linear SVMs as patch-experts, a simplified optimization criterion, and a composite warp update step.
The simplified optimization divides complex registration into N simple warp displacements, estimated via weighted least-squares, enabling parallelization.

Main Results:

The ELS algorithm achieved superior performance to the 'simultaneous' AAM method.
Real-time fitting speeds of 35 fps were attained, significantly faster than 'simultaneous' AAM.
Experiments on the CMU Multi-PIE database validated the method's effectiveness.

Conclusions:

The proposed ELS algorithm offers a significant advancement in non-rigid face registration and tracking.
The method successfully balances high accuracy with real-time performance.
The simplified optimization and parallelization capabilities present a promising direction for future research in facial analysis.