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我创建了一个使用局部二进制模式 (LBP) cv2.face.LBPHFaceRecognizer_create(radius=2, neighbors=10, grid_x=6, grid_y=6)执行人脸识别的简单程序。

我想知道是否有办法用 FAR、FRR 和 ERR 来评估这个模型。

这是我的小程序：

PATH = glob.glob("./DATASET/Face_Recognition/CALTECH_Faces/Only_Face/*")
    #model = cv2.face.LBPHFaceRecognizer_create() #Local Binary Patterns model
    model = cv2.face.LBPHFaceRecognizer_create(radius=2, neighbors=10, grid_x=6, grid_y=6) #best r=2 n=10
    rec = face_detect.haar()
    img_h, img_w = 350, 500 # webcam window size 
    face_cascade = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml")
    cap = cv2.VideoCapture(0)
    final_str = ""

    face_db = [] #I create a list containing all the PATHs to the single images
    for folder in PATH:
        for f in glob.glob(folder+'/*.jpg'): 
            face_db.append(f)

    faces = [] #array that will contain the individual faces
    for img_path in face_db:
        img = cv2.imread(img_path)
        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # LBP works only on B&W images
        faces.append(gray_img)

    ids = np.array([i for i in range(0, len(faces))]) # np array np with indices from 0 to total number of images

    model.train(faces, ids) #addestro il modello
    print("Create the model...\n")
    model.save("./DATASET/Face_Recognition/CALTECH_Faces/Only_Face/1_LBPH_model.yml")
    print("The model is ready. Accendo la webcam\n")
    model.read("./DATASET/Face_Recognition/CALTECH_Faces/Only_Face/1_LBPH_model.yml")
    #histograms = model.getHistograms() 

    # apply HAAR 
    while(cap.isOpened()): # I go through it as long as the webcam is on
        _, frame = cap.read()
        frame = cv2.resize(frame, (img_w, img_h))
        results = rec.process_frame(frame)
        for res in results:
            img = frame[res[1]:res[1]+res[3], res[0]:res[0]+res[2]]
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            label, confidence = model.predict(gray)

            # since label contains all the path of the image for example
             # "./DATASET/Face_Recognition/CALTECH_Faces/Only_Face/Andrea/Andrea_1.jpg" I just take the name of the folder, to do it
             # I take from the 51st character of label (corresponds to the first letter of the folder, before it was Andrea) then
             # using extract_name, take the rest of the name up to the character before the "/".
            who_is = str(face_db[label])
            who_is = who_is[51:]
            final_str = extract_name(who_is)

            # the perfect match occurs with a confidence lower than 100
            final_str = final_str if confidence<100 else "Unknow"
            color = (0,255,0) if confidence<100 else (0,0,255)
            label_to_show = final_str + " Conf.: " + str(round(confidence))
            rec.print_rectangle(frame,res,label_to_show, color)

        cv2.imshow("Biometrics", frame)  
        k = cv2.waitKey(1)  
        if(k%256 ==ord("q")):
            break

        #print("Soggetto: ", final_str)
        #print("Confidence: ", confidence)

def extract_name(who):
    final_str = ""
    for i in who:
        if(i != "/"):
            final_str += i
        else: 
            return final_str
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