Head pose estimation from a 2D face image using 3D face morphing with depth parameters.

Area of Science:

• Computer Vision
• Biometrics
• 3D Face Modeling

Background:

• Accurate head pose estimation is crucial for human-computer interaction and surveillance.
• Existing methods often struggle with variations in lighting, expression, and pose from 2D images.
• 3D face models offer a more robust representation but require accurate fitting to 2D data.

Purpose of the Study:

• To develop a computationally efficient method for estimating head pose angles from a single 2D face image.
• To utilize a morphed 3D face model tailored to the query subject for improved accuracy.
• To minimize the disparity between 2D facial features and projected 3D model points.

Main Methods:

• A reference 3D face model (same ethnicity/gender) is morphed to match prominent facial features of the query 2D face image.
• The morphing process adjusts depth at feature points using a scalar parameter, minimizing 2D-3D feature disparity.
• Optimal depth parameters are found by minimizing projection errors.
• The technique was validated on the USF Human-ID and Pointing'04 databases.

Main Results:

• The proposed method accurately estimates head pose angles from single 2D face images.
• Average head pose estimation errors were as low as 7.93° for nodding and 4.65° for shaking angles.
• The morphing process enhances specificity to the query face, especially with multiple training images.

Conclusions:

• The developed head pose estimation technique is effective and computationally efficient.
• Morphed 3D face models provide a robust approach for single-image head pose estimation.
• The method demonstrates significant potential for applications requiring accurate head pose analysis.