Review: Single attribute and multi attribute facial gender and age estimation.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Facial age and gender recognition are crucial for applications like consumer profiling, advertising, human-computer interaction, and security.
• Accurate demographic profiling enhances personalized user experiences and targeted marketing strategies.
• Existing research spans conventional and deep learning methods, each with unique strengths and limitations.

Purpose of the Study:

• To conduct a comprehensive review of facial age estimation and gender classification techniques.
• To analyze single-attribute (gender or age) and multi-attribute (gender and age) prediction models.
• To provide insights into the pros, cons, and future research avenues for facial recognition technologies.

Main Methods:

• Review of conventional machine learning algorithms for facial attribute recognition.
• Analysis of deep learning architectures applied to facial age and gender prediction.
• Compilation and comparison of benchmark datasets for evaluating model performance in diverse environments.

Main Results:

• Identification of key conventional and deep learning methods for facial age and gender recognition.
• Evaluation of the advantages and disadvantages of different approaches.
• Summary of relevant databases for benchmarking, detailing their properties for constrained and unconstrained scenarios.

Conclusions:

• Facial recognition technology offers significant potential across various industries.
• Deep learning methods show promise but require further research for optimal performance.
• A standardized approach to dataset evaluation is needed for robust benchmarking.