基于opencv的人脸识别

LBPH人脸识别

import cv2
import numpy as np
images=[]

#刘诗诗
images.append(cv2.imread("./lss/1.png",0))
images.append(cv2.imread("./lss/2.png",0))
images.append(cv2.imread("./lss/3.png",0))
images.append(cv2.imread("./lss/4.png",0))
images.append(cv2.imread("./lss/5.png",0))

#刘亦菲
images.append(cv2.imread("./lyf/1.jpg",0))
images.append(cv2.imread("./lyf/2.jpg",0))
images.append(cv2.imread("./lyf/3.jpg",0))
images.append(cv2.imread("./lyf/4.jpg",0))
images.append(cv2.imread("./lyf/5.jpg",0))

#标签
labels=[0,0,0,0,0,1,1,1,1,1]

#获取识别器
recognizer = cv2.face.LBPHFaceRecognizer_create()

#训练
recognizer.train(images, np.array(labels))

#待识别照片
predict_image=cv2.imread("001.jpg",0)

#识别
label,confidence= recognizer.predict(predict_image)

print("label=",label)
print("confidence=",confidence)

confidence为识别结果与模型之间的距离，0表示百分百准确，正常情况下小于50都可以认为可信，还是要根据项目的实际情况去跑大量测试来确定阈值。

EigenFaces人脸识别

import cv2
import numpy as np
images=[]

#刘诗诗
images.append(cv2.imread("./lss/1.jpg",0))
images.append(cv2.imread("./lss/2.jpg",0))
images.append(cv2.imread("./lss/3.jpg",0))
images.append(cv2.imread("./lss/4.jpg",0))
images.append(cv2.imread("./lss/5.jpg",0))

#刘亦菲
images.append(cv2.imread("./lyf/1.jpg",0))
images.append(cv2.imread("./lyf/2.jpg",0))
images.append(cv2.imread("./lyf/3.jpg",0))
images.append(cv2.imread("./lyf/4.jpg",0))
images.append(cv2.imread("./lyf/5.jpg",0))

#标签
labels=[0,0,0,0,0,1,1,1,1,1]


#获取识别器
recognizer = cv2.face.EigenFaceRecognizer_create()

#训练
recognizer.train(images, np.array(labels))


#待识别照片
predict_image=cv2.imread("10.jpg",0)

#识别
label,confidence= recognizer.predict(predict_image)

print("label=",label)
print("confidence=",confidence)

confidence为识别结果与模型之间的距离，0表示百分百准确，正常情况下小于5000都可以认为可信。

Fisherfaces人脸识别

import cv2
import numpy as np
images=[]

#刘诗诗
images.append(cv2.imread("./lss/1.jpg",0))
images.append(cv2.imread("./lss/2.jpg",0))
images.append(cv2.imread("./lss/3.jpg",0))
images.append(cv2.imread("./lss/4.jpg",0))
images.append(cv2.imread("./lss/5.jpg",0))

#刘亦菲
images.append(cv2.imread("./lyf/1.jpg",0))
images.append(cv2.imread("./lyf/2.jpg",0))
images.append(cv2.imread("./lyf/3.jpg",0))
images.append(cv2.imread("./lyf/4.jpg",0))
images.append(cv2.imread("./lyf/5.jpg",0))

#标签
labels=[0,0,0,0,0,1,1,1,1,1]


#获取识别器
recognizer = cv2.face.FisherFaceRecognizer_create()

#训练
recognizer.train(images, np.array(labels))


#待识别照片
predict_image=cv2.imread("10.jpg",0)

#识别
label,confidence= recognizer.predict(predict_image)

print("label=",label)
print("confidence=",confidence)

confidence为识别结果与模型之间的距离，0表示百分百准确，正常情况下小于5000都可以认为可信。