Face Detection App

Technology Stack

The project utilized state-of-the-art technologies, leveraging deep learning frameworks such as TensorFlow and PyTorch. The chosen frameworks offer pre-trained models suitable for face detection tasks, streamlining the development process.

Model Selection

The team opted for a Convolutional Neural Network (CNN) based model due to its efficiency in image recognition tasks. Transfer learning was employed, utilizing a pre-trained model and fine-tuning it on a custom dataset to adapt it specifically for face detection.

Integration with Mobile Platforms

For seamless integration with Android and iOS, the team developed native modules using Android Studio (for Android) and Xcode (for iOS). This ensured optimal performance and native user experiences on both platforms.

Real-time Processing

The implementation focused on achieving real-time face detection, requiring optimization for mobile devices' limited resources. Model quantization and efficient memory management were key strategies to address this challenge.