Age, Gender, and Emotion Detection

This project involved building a real-time face detection system that could identify a person’s age, gender, and emotions using a convolutional neural network (CNN) model. By leveraging OpenCV for face detection and preprocessing and Keras/TensorFlow for training deep learning models, the system successfully classified these features with high accuracy.

Impact: This system can be applied in a wide range of fields, from marketing (understanding customer demographics) to security (monitoring emotional behavior in real-time).

Challenges and Achievements: One of the major challenges was achieving high accuracy across all three classification tasks simultaneously, which I tackled by fine-tuning the CNN architecture and using data augmentation techniques.