Day #329 - Face Re-Identification

First Approach (Blog Posted)
1. Haar based face extraction
2. Dlib for comparison
3. Custom models for Age / Gender

Second Approach (Blog posted)
Custom openvino models

Third Approach 
This link was useful

pip install easyfacenet

Key Concepts
1. Alignment
2. Feature Extraction
3. Comparison

	#pip install easyfacenet
	#paper - https://arxiv.org/pdf/1503.03832.pdf
	#https://github.com/taflahi/facenet/blob/master/simple/facenet.py
	from easyfacenet.simple import facenet
	images = [r'E:\Learning_Days_2020\Segmentation\face1.jpg',r'E:\Learning_Days_2020\Segmentation\face2.jpg',r'E:\Learning_Days_2020\Segmentation\face1.jpg']
	aligned = facenet.align_face(images)
	comparisons = facenet.compare(aligned)
	print('Image 1 and 2 comparison results')
	print(comparisons[0][1])
	print('Image 1 and 3 comparison results')
	print(comparisons[0][2])
	embeddings = facenet.embedding(aligned)
	print(embeddings)
	#More Reads
	#http://dlib.net/face_alignment.py.html
	#http://dlib.net/face_clustering.py.html
	#http://dlib.net/face_detector.py.html
	#http://dlib.net/face_landmark_detection.py.html
	#http://dlib.net/face_recognition.py.html
	#https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/
	#https://medium.com/data-science-lab-amsterdam/face-recognition-with-python-in-an-hour-or-two-d271324cbeb3
	#https://robotzero.one/face-recognition-party-greeter-raspberry-pi/
	#https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65

view raw face_comparison.py hosted with ❤ by GitHub

Happy Learning!!!