Python在人脸识别方面功能很强大，程序语言简单高效，下面小编来编程实现一下如何实现人脸识别。如有错点，还望斧正

识别图片中的人脸位置

#人脸识别分类器路径tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'#人脸定位函数def face_detect(img):    gray = cv.cvtColor(img,cv.COLOR_BGRA2BGR)    face = cv.CascadeClassifier(tool_url)    #将图片转化为灰度图    faces = face.detectMultiScale(gray)    #确定人脸部分    for x,y,w,h in faces:        reg = cv.rectangle(img, (x, y),( x + w, y + h), color=(0, 255, 0), thickness=2)    cv.imshow("reg",reg)

根据训练集训练数据并保存(dataTraining.py)

import osimport cv2 as cvimport numpyfrom PIL import Image'''训练数据来自s2和s9'''def getImageAndIds(path):    facesSimples = []    ids = []    img_paths = [os.path.join(path,f) for f in os.listdir(path)]    #检测人脸    tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'    # 调用插件获取图像特征    face = cv.CascadeClassifier(tool_url)    for i in img_paths:        # print(i)        img = Image.open(i).convert("L")        # Image._show(img)        #将图片转换为数组        img_np = numpy.array(img,'uint8')        # print(img_np)        # 获取图像的人脸信息和对应id        faces = face.detectMultiScale(img_np)        id = int(i.split(path)[1].split('.')[0])for x, y, w, h in faces:            facesSimples.append(img_np[y:y+h,x:x+w])            ids.append(id)    return facesSimples,idsif __name__ == "__main__":    #图片路径    path = "./data/"    faces,ids = getImageAndIds(path)    #获取循环对象    reg = cv.face.LBPHFaceRecognizer_create()    reg.train(faces,numpy.array(ids))    #保存文件    reg.write("trainer/trainer.yml")

人脸识别(faceChecking.py)

import cv2 as cvimport os,numpy#检测人脸tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'#图片数据路径path = './data/'# 调用插件获取图像特征face = cv.CascadeClassifier(tool_url)#加载训练数据reg = cv.face.LBPHFaceRecognizer_create()reg.read("trainer/trainer.yml")img= cv.imread("3.jpg")#将图像的人脸特征圈出来gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)faces = face.detectMultiScale(gray)for x, y, w, h in faces:    # print(x,y,w,h)    cv.rectangle(gray, (x, y), (x + w, y + h), color=(0, 0, 255), thickness=2)    #检测出识别后的图片和id    id,configence = reg.predict(gray[y:y+h,x:x+w])    #显示对应图片    print("编号:{},置信度:{}".format(id,configence))    aim_img = cv.imread(os.path.join(path,str(id)+'.jpg'))    #显示目标图片    cv.imshow("aim_img",aim_img)cv.imshow("result",gray)cv.waitKey(0)cv.destroyAllWindows()