Tools And Technologies: Python, FastAPI, Machine Learning, PyTorch/TensorFlow
Project Solution Approach: For this facial recognition project, choose any of the several publicly available facial recognition datasets, such as the Labeled Faces in the Wild (LFW) dataset, the CelebA dataset, and the FaceNet dataset. Next, you will create a new FastAPI application using a command-line interface or a Python code editor. You can preprocess the images in the chosen dataset using Python libraries such as OpenCV or Pillow. You can perform operations such as resizing, cropping, and normalization. Then, you will use a machine learning algorithm such as Convolutional Neural Networks (CNN) and popular deep learning frameworks like TensorFlow or PyTorch to train your model on the preprocessed dataset. You will then define API endpoints using FastAPI's decorator syntax, specifying the request method and the response model. For example, you can define an endpoint to recognize a face in an image and return the individual’s name.
