Age and Gender Detection

CNN models for age and gender classification in facial images using deep learning optimization techniques.

Overview

I designed and optimized CNN models to tackle the challenging problem of age and gender detection from facial images. This project was all about exploring how deep learning can be applied to computer vision tasks, comparing different approaches and pushing for high accuracy in demographic classification from visual data.

Technical Approach

Model Development

  • Architecture: Built custom CNN models using Keras and TensorFlow for image classification
  • Optimization Focus: Experimented with various model architectures and optimization techniques
  • Comparative Analysis: Evaluated both traditional computer vision methods and modern deep learning approaches

What I Worked On

  • CNN Design: Created and fine-tuned convolutional neural network architectures for facial analysis
  • Model Optimization: Implemented various optimization strategies to improve accuracy and efficiency
  • Data Processing: Developed preprocessing pipelines for facial image data preparation
  • Performance Comparison: Systematically compared traditional vs deep learning approaches to understand their trade-offs

Results

The project successfully achieved:

  • High accuracy in both age and gender classification tasks
  • Clear insights into the advantages of deep learning over traditional computer vision methods
  • Optimized model architectures that balance performance with computational efficiency
  • A comprehensive understanding of the challenges in demographic classification from images

Why This Matters

Age and gender detection has wide applications in marketing analytics, user experience personalization, security systems, and demographic research. This project helped me understand the complexities of working with facial data, the importance of model optimization, and the ethical considerations around demographic classification systems.

Project Duration: November 2020 – December 2020