Recognize faces using OpenCV and Python.

• Install OpenCV with Python bindings.
• Clone and get started -

  $ git clone git@github.com:singhpratyush/face-the-gate.git
  $ cd face-the-gate/
  $ mkdir rsc/images

• While in src, use add_data.py to add new face data.
• Arguments -
  • -c | --camera-id - Camera device ID. Defaults to 0.
  • -i | --subject-id - ID of subject whise data is to be added.
  • -s | --start-pos - Position of start index for the subject ID.
  • -e | --end-pos - Position of end index for the subjet ID.

• While in src, use main.py to test the data collected. You must have atleast 2 subjects registered to start this activity.
• Make sure to use -r or --refresh-data option to rebuild the classification data from the images.
• You may use the -c or --camera-id to specify the camera ID if default is not 0.

Uses 3 different classifiers -

• Eigen Face Recognizer
• Fisher Face Recognizer
• LBPH Face Recognizer

Sometimes, camera is little bent and the resulting image of face is little bent too. But this small tilt has a large impact on the recognition numbers. To overcome this, following technique is used -

• Get the position of eyes in the frame.
• Calculate the angle by which they are away from being on the same horizontal line.

    delta_y = right_eye_y_center - left_eye_y_center
    delta_x = right_eye_x_center - left_eye_x_center
    rotation_degrees = math.degrees(math.atan(float(delta_y) / (delta_x)))

• Generate a 2D rotation matrix for the corresponding canvas and rotation angle.
• Perform affine transform using the rotation matrix.