Image-Based Age Group Classification and Quality Assurance for AI Systems
Worked on a project supporting computer vision AI systems by reviewing and labeling image data to classify human age groups based on visual guidelines. The scope of the project involved analyzing facial and physical features in images and assigning accurate age group labels while maintaining strict consistency across large datasets. Performed image-level classification tasks using predefined age categories, carefully following visual and quality guidelines to minimize bias and labeling errors. Reviewed edge cases where age estimation was ambiguous and applied standardized decision rules to ensure labeling accuracy and reliability. Participated in quality assurance processes by double-checking annotations, identifying inconsistencies, and correcting misclassified samples. Adhered to project accuracy thresholds, review standards, and audit requirements to maintain high-quality labeled data suitable for training and evaluating AI models.