简单尝试

给自己拍张照

from time import sleep
from picamera import PiCamera
camera=PiCamera()
camera.resolution=(320,240)
camera.start_preview()
sleep(2)
camera.capture('photo.jpg')

这里考虑到树莓派的计算能力，分辨率选择320，240
如果对于速度要求不高，可以选择更高分辨率，需要对后面的代码进行相应修改

把自己添加到识别队列中

# This is a demo of running face recognition on a Raspberry Pi.
# This program will print out the names of anyone it recognizes to the console.
# To run this, you need a Raspberry Pi 2 (or greater) with face_recognition and
# the picamera[array] module installed.
# You can follow this installation instructions to get your RPi set up:
# https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65
import face_recognition
import picamera
import numpy as np
# Get a reference to the Raspberry Pi camera.
# If this fails, make sure you have a camera connected to the RPi and that you
# enabled your camera in raspi-config and rebooted first.
camera = picamera.PiCamera()
camera.resolution = (320, 240)
output = np.empty((240, 320, 3), dtype=np.uint8)
# Load a sample picture and learn how to recognize it.
print("Loading known face image(s)")
obama_image = face_recognition.load_image_file("obama_small.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
lingxiao_image = face_recognition.load_image_file("LingxiaoWang.jpg")
lingxiao_face_encoding = face_recognition.face_encodings(lingxiao_image)[0]
# Initialize some variables
face_locations = []
face_encodings = []
while True:
    print("Capturing image.")
    # Grab a single frame of video from the RPi camera as a numpy array
    camera.capture(output, format="rgb")
    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(output)
    print("Found {} faces in image.".format(len(face_locations)))
    face_encodings = face_recognition.face_encodings(output, face_locations)
    # Loop over each face found in the frame to see if it's someone we know.
    for face_encoding in face_encodings:
        # See if the face is a match for the known face(s)
        match = face_recognition.compare_faces([obama_face_encoding,lingxiao_face_encoding], face_encoding)
        name = "<Unknown Person>"
        if match[0]:
            name = "Barack Obama"
        if match[1]:
            name = "Lingxiao Wang"
        print("I see someone named {}!".format(name))

这样树莓派便可以识别出我和奥巴马两个人了 ：）

利用opencv进行显示

进一步修改代码，利用opencv把自己圈出来

# This is a demo of running face recognition on a Raspberry Pi.
# This program will print out the names of anyone it recognizes to the console.
# To run this, you need a Raspberry Pi 2 (or greater) with face_recognition and
# the picamera[array] module installed.
# You can follow this installation instructions to get your RPi set up:
# https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65
import face_recognition
import picamera
import numpy as np
import cv2
# Get a reference to the Raspberry Pi camera.
# If this fails, make sure you have a camera connected to the RPi and that you
# enabled your camera in raspi-config and rebooted first.
camera = picamera.PiCamera()
camera.resolution = (320, 240)
frame = np.empty((240, 320, 3), dtype=np.uint8)
output = np.empty((240, 320, 3), dtype=np.uint8)
# Load a sample picture and learn how to recognize it.
print("Loading known face image(s)")
obama_image = face_recognition.load_image_file("obama_small.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
lingxiao_image = face_recognition.load_image_file("LingxiaoWang.jpg")
lingxiao_face_encoding = face_recognition.face_encodings(lingxiao_image)[0]
# Initialize some variables
face_locations = []
face_encodings = []
while True:
    face_names=[]
    print("Capturing image.")
    # Grab a single frame of video from the RPi camera as a numpy array
    camera.capture(frame, format="bgr")
    #这里需要注意的是，opencv使用的是bgr格式的图像，要转换成rgb格式才能用来识别
    frame=frame.reshape((240,320,3))
    output=frame[:,:,::-1]
    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(output)
    print("Found {} faces in image.".format(len(face_locations)))
    face_encodings = face_recognition.face_encodings(output, face_locations)
    # Loop over each face found in the frame to see if it's someone we know.
    for face_encoding in face_encodings:
        # See if the face is a match for the known face(s)
        match = face_recognition.compare_faces([obama_face_encoding,lingxiao_face_encoding], face_encoding)
        name = "<Unknown Person>"
        if match[0]:
            name = "Barack Obama"
        if match[1]:
            name = "Lingxiao Wang"
        print("I see someone named {}!".format(name))
        face_names.append(name)
    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
    # Display the resulting image
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

我自己
我和奥巴马（手动滑稽）

帅到模糊的学长

另外，此模型对于亚洲人脸识别准确率有待提高，可能会出现张冠李戴的情况。

opencv的玄学

安装opencv是个玄学，碰到问题自行百度，安装相应的库即可
注意默认pip版本，默认python版本