LBPHFaceRecognizer slows down processes

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Hi to all, i'm using RaspberryPI4+Python 3.7+ openCV to move servo motors. I have seen that using LBPHFaceRecognizer the program is much slower. Do you think it's normal or something in the code that slows it down? Using only Haar cascade_Frontalface servo motors are very fast, but if at the same time i want to use LBPHFaceRecognizer they are very slow..:

#!/usr/bin/python
import cv2
import os
import numpy as np
import time
import math

from adafruit_servokit import ServoKit

capture = cv2.VideoCapture(0)
capture.set(3,320)
capture.set(4,240)

#start position

kit = ServoKit(channels=8)
ang=90

kit.servo[0].angle = ang
time.sleep(1)

angy=90
kit.servo[1].angle = angy
time.sleep(1)

#functions
def faceDetection(img):   
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    
    haar_face=cv2.CascadeClassifier('/home/pi/CV/haarcascade_frontalface_default.xml')    
    face=haar_face.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=8)

    return face,gray

def training_data(directory):   
    faces=[]   
    facesID=[]

    for path,subdir,filename in os.walk(directory):        
        for filename in filename:           
            if filename.startswith("."):               
                print("skipping system file")            
                continue
            id=os.path.basename(path)

            image_path=os.path.join(path,filename)            
            img_test=cv2.imread(image_path)

            if img_test is None:                
                print ("error opening image")               
                continue

            face,gray=faceDetection(img_test)           
            if len(face)>0:                
                continue

            (x,y,w,h)=face[0]           
            region=gray[y:y+w, x:x+h]           
            faces.append(region)            
            facesID.append(int(id))

    return faces, facesID

def train_classifier(faces,facesID):    
    face_recognizer=cv2.face.LBPHFaceRecognizer_create()    
    face_recognizer.train(faces,np.array(facesID))

    return face_recognizer

def put_name(test_img,text,x,y,w,h):    
    cv2.putText(test_img,text,(x,y),cv2.QT_FONT_NORMAL,0.5,(0,0,255),1)

#code

faceRecognizer=cv2.face.LBPHFaceRecognizer_create()
faceRecognizer.read('/home/pi/CV/trainedData.yml')

name={0:"Gian", 1:"Gian"}

while True:
    ret,test_img=capture.read()    
    faces_detected,gray=faceDetection(test_img)   
    for face in faces_detected:       
        (x,y,w,h)=face        
        region=gray[y:y+w, x:x+h]

        label, confidence=faceRecognizer.predict(region)       
        predictedName=name[label]  
        put_name(test_img, predictedName,x,y,w,h) 

    resized = cv2.resize(test_img,None,fx=0.5,fy=0.5)    
    cv2.imshow("face",test_img)
    cv2.waitKey(1)

    if (x <= 109 and len(faces_detected)>0):       
        if x<=39:           
            ang+=3            
        elif x<=89 and x>39:            
            ang+=2           
        else:         
            ang+=1        
        kit.servo[0].angle = ang        
        time.sleep(0.001)        
    elif (x >= 151 and len(faces_detected)>0):        
        if x>=220:            
            ang-=3            
        elif x>=171 and x<220:            
            ang-=2            
        else:            
            ang-=1

        kit.servo[0].angle = ang        
        time.sleep(0.001)

    if (y <= 60 and len(faces_detected)>0):    
        if x<=20:            
            angy+=2        
        else:           
            angy+=1    
        kit.servo[1].angle = angy        
        time.sleep(0.001)   
    elif (y >= 90 and len(faces_detected)>0):        
        if y>=130:           
            angy-=2           
        else:            
            angy-=1

        kit.servo[1].angle = angy        
        time.sleep(0.001)