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Deep Learning Project

https://projectgurukul.org/deep-learning-project-face-recognition-with-python-opencv/

Sudhanshi
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Deep Learning Project

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  1. ProjectGurukul Deep Learning Project Deep Learning Project – Face Recognition with Python & OpenCV

  2. Deep Learning Project Face Recognition with Python – Identify and recognize a person in the live real- time video. In this deep learning project, we will learn how to recognize the human faces in live video with Python. We will build this project using python dlib’s facial recognition network. Dlib is a general-purpose software library. Using dlib toolkit, we can make real-world machine learning applications. In this project, we will first understand the working of face recognizer. Then we will build face recognition with Python.

  3. About dlib’s Face Recognition: Python provides face_recognition API which is built through dlib’s face recognition algorithms. This face_recognition API allows us to implement face detection, real-time face tracking and face recognition applications. Project Prerequisites: You need to install the dlib library and face_recognition API from PyPI: pip3 install dlib pip3 install face_recognition Steps to implement Face Recognition with Python: We will build this python project in two parts. We will build two different python files for these two parts: embedding.py: In this step, we will take images of the person as input. We will make the face embeddings of these images. recognition.py: Now, we will recognize that particular person from the camera frame.

  4. F A C E R E C O G N I T I O N W I T H P Y T H O N :

  5. Summary: This deep learning project teaches you how to develop human face recognition project with python libraries dlib and face_recognition APIs (of OpenCV). It also covers the introduction to face_recognition API. We have implemented this python project in two parts: In the first part, we have seen how to store the information about human face structure, i.e face embedding. Then we learn how to store these embeddings. In the second part, we have seen how to recognize the person by comparing the new face embeddings with the stored one.

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