Inspiration
The rapid growth of Artificial Intelligence in healthcare inspired us to build a system that can automatically analyze facial images to predict age and gender and provide useful healthcare and behavioral insights. What it does
The Multi-Modal Age and Gender Classification with Healthcare and Behavioral Analysis system uses AI and deep learning to detect a person’s face from live webcam or images, and then predicts:
Age group Gender Basic behavioral insights Potential healthcare-related observations
How we built it
We built the project using machine learning, deep learning, and web technologies. Main technologies used: Python for model development TensorFlow / Keras for building the CNN model OpenCV for face detection and image processing Flask / Web framework for backend integration HTML, CSS, JavaScript for the user interface Webcam integration for real-time detection Process:
Collected and prepared a facial image dataset for age and gender classification. Preprocessed images (resizing, normalization). Built and trained a CNN deep learning model. Implemented face detection using OpenCV.
Challenges we ran into While developing the project, we faced several challenges: Dataset imbalance affecting prediction accuracy. Difficulty in achieving high accuracy for age prediction. Handling different lighting conditions and facial angles.
What we learned Through this project, we learned: Practical implementation of deep learning models using CNNs. Image processing and face detection using OpenCV. Integration of AI models with web applications.
In the future, we plan to enhance the system by:
Developing a mobile application for wider accessibility.
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