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Detect faces. Compute 128-d face embeddings to quantify a face. Train a Support Vector Machine (SVM) on top of the embeddings. Recognize faces in images and video streams.

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FACE-DETECTION-USING-DEEP-LEARNING-AND-OPEN-CV

In python.txt you can get to know how to execute the code once its been cloned or downloaded as a zip.

We need numpy, argparse, cv2, imutils libraries which can be installed using pip [https://pip.pypa.io/en/stable/] or conda [https://docs.conda.io/en/latest/] command in anaconda prompt.

If you don't have anaconda you can get it installed from here- [https://www.anaconda.com/distribution/]

Following are the commands that can be used to install the libraries using pip.

  • For numpy > pip install numpy

  • For argparse > pip install argparse

  • For cv2 > pip install opencv-python

  • For imutils > pip install imutils

From the above pip installation commands you can install the libraries from [https://pypi.org/]

If you need to know about res10_300x300_ssd_iter_140000.caffemodel you can refer (https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector/how_to_train_face_detector.txt)

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Detect faces. Compute 128-d face embeddings to quantify a face. Train a Support Vector Machine (SVM) on top of the embeddings. Recognize faces in images and video streams.

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