Age and Gender Detection Project Guide

Detecting the age and gender of a person from facial images has wide applications — from targeted advertising and social analytics to access control and personalized content delivery. Manual estimation is subjective and unreliable. Deep learning models trained on facial datasets can automate this task with high accuracy. Developing such a model teaches you important skills in face detection, image classification, and fine-grained feature extraction from real-world noisy images.

By detecting a face and analyzing its features using a Convolutional Neural Network (CNN), we can predict both age group (like child, adult, senior) and gender (male, female). Models like MobileNet, VGGFace, or custom-built lightweight CNNs are used. Proper data augmentation, balancing age/gender classes, and face alignment techniques enhance model robustness across ethnicities, lighting conditions, and expressions, making your model accurate in real-world applications.

The system uses a face detector (like MTCNN or Haar Cascades) to isolate faces from images or video streams. These cropped faces are passed through CNN models trained separately for age and gender classification tasks. Post-processing assigns age ranges and gender probabilities to detected faces, supporting real-time or batch processing. Fine-tuning pre-trained models dramatically improves accuracy even with moderately sized datasets, enabling practical deployment on mobile or cloud platforms.

• Collect datasets like UTKFace, Adience, or FG-NET containing labeled face images by age and gender.
• Preprocess images: align faces, normalize lighting conditions, and apply augmentation techniques.
• Train CNN models for gender (binary classification) and age (multi-class classification or regression).
• Evaluate using metrics like accuracy for gender and mean absolute error (MAE) for age predictions.
• Deploy the model into web or mobile apps for real-time demographic analysis from camera feeds.

• UTKFace Dataset
• Adience Benchmark Age & Gender Dataset
• TensorFlow Transfer Learning Guide