请问如何提高人脸识别与笑脸识别的识别率呢，谢谢

需要人脸检测haar和笑脸检测ccn的详细资料，以及简介

@kidswong999 谢谢

import sensor, image, os, nn, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3

sensor.reset()                          # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)     # Set pixel format to RGB565
sensor.set_framesize(sensor.QVGA)       # Set frame size to QVGA (320x240)
sensor.skip_frames(time=2000)
sensor.set_auto_gain(False)

# Load smile detection network
net = nn.load('/smile.network')

# Load Face Haar Cascade
face_cascade = image.HaarCascade("frontalface", stages=25)

while (True):
    pyb.LED(RED_LED_PIN).on()
    print("About to start detecting smile...")
    sensor.skip_frames(time = 2000) # Give the user time to get ready.

    pyb.LED(RED_LED_PIN).off()
    print("Now detecting smile!")
    pyb.LED(BLUE_LED_PIN).on()

    diff = 10 # We'll say we detected a face after 10 frames.
    while(diff):
        img = sensor.snapshot()

    # Find faces.
        faces = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
        if faces:
           diff -= 1

    # Detect smiles
           for r in faces:
        # Resize and center detection area
              r = [r[0]+10, r[1]+25, int(r[2]*0.70), int(r[2]*0.70)]
              img.draw_rectangle(r)
              out = net.forward(img, roi=r, softmax=True).
              img.draw_string(r[0], r[1], ':)' if (out[0] > 0.65) else ':(', color=(255), scale=2)
    pyb.LED(BLUE_LED_PIN).off()

    print("smile detected! Saving image...")
      sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.