Face Recognition using OpenCV and Python.

Face recognition is becoming increasingly important as our identities are digitalized. We are moving away further from the passwords and PIN numbers. Today, biometric sensors are available on all security devices and smartphones.
Innovation such as biometric is widely used now in place of the PIN and password. However facial recognition has started to rule these and will offer another level of security and flexibility.

Let's learn how face recognition works!!

Face Recognition works in 4 steps:

1. Detect face in image.
2. Compute Facial features
3. Compare facial feature against known faces.
4. Make a prediction.

But before Recognizing any face , we need to train our system , how to identify and recognise a face.Hence first, I will first discuss how to train our classifier.

Thanks to OpenCV , it makes the face recognition easy for any beginner. OpenCV comes with three built in face recognizers algorithms.

• Eigen Faces : cv2.face.createEigenFaceRecognizer()
• FisherFaces:  cv2.face.createFisherRecognizer()
• Local Binary Patterns Histograms: cv2.face.createLBPHFaceRecognizer()

Implementation in Python: 

Let's go ahead and dive into some code.

Training the classifier

	import cv2 , os
	import numpy as np
	#-------FUNCTIONS ------------------#
	def detectFace(img):
	faces = faceCascade.detectMultiScale(img, 1.3 , minSize = (200,200) )
	x,y,w,h = faces[0]
	faceImg = img [ y:y+h , x:x+w ]
	return faceImg
	# load trained XML file
	cascadePath = "haarcascade_frontalface_default.xml"
	faceCascade = cv2.CascadeClassifier(cascadePath)
	# We will be using LBPH
	recognizerLBP = cv2.createEigenFaceRecognizer()
	#recognizerEigenFace = cv2.createLBPHFaceRecognizer()
	#recognizerFisher = cv2.createFisherFaceRecognizer()
	# load all face images
	allImgPath = "imgs"
	faceImgs = []
	faceLabels = []
	for label in os.listdir(allImgPath):
	imgPath = os.path.join(allImgPath,label)
	# load imgs of each subject
	for name in os.listdir(imgPath):
	img = cv2.imread(os.path.join(imgPath , name))
	gray = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY)
	gray = cv2.resize(gray ,(0,0), fx = .2 ,fy = .2)
	faceImg = detectFace(gray)
	faceImg = cv2.resize(faceImg ,(500,500))
	faceImgs.append(faceImg)
	faceLabels.append(int(label))
	#cv2.imshow("img" , faceImg )
	#cv2.waitKey(10)
	# ------------------------------------Start Training-------------------------------------#
	recognizerLBP.train(faceImgs, np.array(faceLabels))
	#recognizerEigenFace.train(faceImgs, np.array(faceLabels))
	#recognizerFisher.train(faceImgs, np.array(faceLabels))
	recognizerLBP.save("trainedFacesLBP.xml")
	#recognizerEigenFace.save("trainedFacesEigen.xml")
	#recognizerFisher.save("trainedFacesFisher.xml")
	print ("Training finished ")

view raw train_faces.py hosted with ❤ by GitHub

Predicting

	import cv2 , os
	import numpy as np
	def detectFace(img):
	faces = faceCascade.detectMultiScale(img, 1.3 , minSize = (200,200) )
	x,y,w,h = faces[0]
	faceImg = img [ y:y+h , x:x+w ]
	faceImg = cv2.resize(faceImg ,(500,500))
	return faceImg
	# load trained XML file
	cascadePath = "haarcascade_frontalface_default.xml"
	faceCascade = cv2.CascadeClassifier(cascadePath)
	recognizerLBP = cv2.createEigenFaceRecognizer()
	recognizerEigenFace = cv2.createLBPHFaceRecognizer()
	recognizerFisher = cv2.createFisherFaceRecognizer()
	recognizerLBP.load("trainedFacesLBP.xml")
	#recognizerEigenFace.load("trainedFacesEigen.xml")
	#recognizerFisher.load("trainedFacesFisher.xml")
	img = cv2.imread("test6.jpg")
	gray = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY )
	gray = cv2.resize(gray ,(0,0), fx = .2 ,fy = .2)
	faceImg = detectFace(gray)
	predLBP = recognizerLBP.predict(faceImg)
	#predEigen = recognizerEigenFace.predict(faceImg)
	#predFisher= recognizerFisher.predict(faceImg)
	print "LBP RESULT : " ,predLBP
	#print "Eigen RESULT : " ,predEigen
	#print "Fisher RESULT : " ,predFisher
	#cv2.destroyAllWindows()

view raw face_recognition.py hosted with ❤ by GitHub

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