Day #266 - Face Recognition Examples - Package - face_recognition

Use Cases
1. Extract Face Region
2. Search for Face Match



Deep face recognition with Keras, Dlib and OpenCV

Key Research Papers - Face Recognition

	#pip install face_recognition
	#https://github.com/zeynepCankara/Face-Recognition-Tensorflow/blob/master/Face_Rec_System.ipynb
	import os
	import face_recognition
	import cv2
	#Get All Images in directory
	images = os.listdir(r'E:\\FaceFeatures\Test')
	print(images)
	#Extract Area of Face
	def ExtractFaceRegion(imagefilepath):
	image = cv2.imread(imagefilepath)
	# Convert it from BGR to RGB
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	# detect face in the image and get its location (square boxes coordinates)
	boxes = face_recognition.face_locations(image, model='hog')
	print(type(boxes))
	top = boxes[0][0]
	right = boxes[0][1]
	bottom = boxes[0][2]
	left = boxes[0][3]
	# draw the predicted face name on the image
	cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
	y= top - 15 if top - 15 > 15 else top + 15
	cv2.putText(image, "Result", (left, y), cv2.FONT_ITALIC,0.75, (0, 255, 0), 2)
	cv2.imshow("Highlighted Image", image)
	cv2.waitKey(0)
	crop = image[top:bottom, left:right]
	cv2.imshow("Cropped Image", crop)
	cv2.waitKey(0)
	encoding = face_recognition.face_encodings(image, boxes)
	print(encoding)
	return
	def SearchforMatch(imagefilepath):
	imagesearch = face_recognition.load_image_file(imagefilepath)
	imagesearchencoded = face_recognition.face_encodings(imagesearch)[0]
	for imagename in images:
	current_image = face_recognition.load_image_file(r'E:\\FaceFeatures\Test\\'+imagename)
	current_image_encoded = face_recognition.face_encodings(current_image)[0]
	result = face_recognition.compare_faces([imagesearchencoded],current_image_encoded)
	if result[0]==True:
	print("Matched:"+imagename)
	else:
	print("Not Matched:"+imagename)
	return
	imagefilepath = r'E:\\FaceFeatures\Test\1.jpg'
	ExtractFaceRegion(imagefilepath)
	SearchforMatch(imagefilepath)
	cv2.destroyAllWindows()
	#Example 2
	#pip install mtcnn
	#https://github.com/ipazc/mtcnn
	from mtcnn import MTCNN
	import cv2
	img = cv2.cvtColor(cv2.imread(r"E:\Siva_Learning_Blog_Posts\Face1.jpg"),cv2.COLOR_BGR2RGB)
	detector = MTCNN()
	print(detector.detect_faces(img))
	result = detector.detect_faces(img)
	bounding_box = result[0]['box']
	keypoints = result[0]['keypoints']
	cv2.rectangle(img,
	(bounding_box[0], bounding_box[1]),
	(bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
	(0,155,255),
	2)
	cv2.circle(img,(keypoints['left_eye']), 2, (0,155,255), 2)
	cv2.circle(img,(keypoints['right_eye']), 2, (0,155,255), 2)
	cv2.circle(img,(keypoints['nose']), 2, (0,155,255), 2)
	cv2.circle(img,(keypoints['mouth_left']), 2, (0,155,255), 2)
	cv2.circle(img,(keypoints['mouth_right']), 2, (0,155,255), 2)
	cv2.imwrite(r'E:\Siva_Learning_Blog_Posts\Result.jpg', cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
	print(result)

view raw face_recognition.py hosted with ❤ by GitHub

London bar uses #FacialRecognition
by @tictoc#Tech #Technology #IT

Cc: @evankirstel pic.twitter.com/z2I1mClzU9

— Ronald van Loon (@Ronald_vanLoon) July 14, 2020

Happy Learning!!!