The Hidden Sides of Names--Face Modeling with First Name Attributes.

Area of Science:

• Computer Vision
• Machine Learning
• Biometrics

Background:

• Traditional facial recognition systems often require extensive labeled datasets.
• Developing robust facial attribute classifiers remains a challenge.
• Automated data collection for facial analysis has practical system-building advantages.

Purpose of the Study:

• To introduce a novel representation of facial appearance based on first name similarities.
• To demonstrate the effectiveness of this representation in various facial analysis tasks.
• To develop a practical facial recognition system using automatically acquired internet data.

Main Methods:

• Developed models for 100 common US first names.
• Constructed pairwise first-name classifiers using internet-downloaded images.
• Utilized classification scores as facial attributes for analysis.
• Applied name attributes to gender, age, and face verification tasks.

Main Results:

• Name attributes significantly outperformed chance in predicting first names for test faces.
• Achieved state-of-the-art performance in gender and age classification using automatically gathered data.
• Demonstrated effective face-name association from image captions.
• Showcased the utility for unconstrained face verification.

Conclusions:

• First name similarity provides a powerful and practical facial representation.
• The proposed method enables robust facial analysis without manual data labeling.
• This approach offers significant advantages for real-world facial recognition applications.