A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation

Area of Science:

• Computer Vision
• Machine Learning
• Biometrics

Background:

• Accurate human face image analysis is crucial for various applications.
• Existing methods often lack a unified approach for comprehensive facial understanding.
• Semantic face segmentation is a key component for detailed facial analysis.

Purpose of the Study:

• To propose a unified framework for face image analysis via end-to-end semantic face segmentation.
• To integrate head pose estimation, age classification, and gender recognition within a single model.
• To enhance the accuracy and efficiency of facial attribute recognition.

Main Methods:

• Developed a unified framework for end-to-end semantic face segmentation.
• Utilized Conditional Random Fields (CRFs) for multi-class face segmentation into six parts.
• Employed probability maps from segmentation as features for a Random Decision Forest (RDF) classifier.
• Trained the model on a manually labeled face dataset.

Main Results:

• The proposed framework achieved accurate multi-class face segmentation.
• Probability maps generated from segmentation served as effective feature descriptors.
• The Random Decision Forest classifier demonstrated strong performance for head pose, age, and gender recognition.
• The framework reported superior results compared to existing methods on multiple datasets.

Conclusions:

• The unified framework provides an effective approach for comprehensive face image analysis.
• End-to-end semantic face segmentation is a powerful tool for facial attribute recognition.
• The proposed method offers improved performance in head pose estimation, age classification, and gender recognition.