Enforcing Convexity for Improved Alignment with Constrained Local Models.

Area of Science:

• Computer Vision
• Machine Learning
• Image Processing

Background:

• Constrained local models (CLMs) show promise for non-rigid object alignment and tracking.
• Existing methods face challenges in jointly optimizing global warp updates across local responses.
• Holistic approaches like Active Appearance Models (AAMs) are leading methods.

Purpose of the Study:

• To propose an efficient method for optimizing the global warp update in CLMs.
• To address limitations of general-purpose and graph-based optimizers in CLM applications.
• To improve non-rigid object alignment and tracking performance.

Main Methods:

• Enforcing convexity at each local patch response surface for efficient optimization.
• Developing a novel framework for global warp update optimization in CLMs.
• Demonstrating the relationship between the proposed method and Lucas-Kanade gradient descent.

Main Results:

• The proposed approach efficiently optimizes global warp updates for CLMs.
• The classic Lucas-Kanade method is shown to be a special case of this framework.
• Improved performance in non-rigid face alignment and tracking was achieved on benchmark datasets.

Conclusions:

• The novel convex-enforcing method offers an efficient solution for CLM optimization.
• This framework enhances the capabilities of CLMs for complex alignment tasks.
• The approach demonstrates significant improvements for real-world face alignment and tracking applications.