首先从中这里下载下代码：
https://github.com/ageitgey/face_recognition#face-recognition
然后安装所以必须的组件，我用的Python3.5
进入example里面跑他的demo，主要就是掉了dlib的接口比如：
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
face_landmarks_list = face_recognition.face_landmarks(rgb_frame)
引入了OpenCV稍微改了下显示，camera实时跟踪人脸的特征点，速度奇慢，而且还不准。
代码很简单：
import face_recognition import cv2 # This is a super simple (but slow) example of running face recognition on live video from your webcam. # There‘s a second example that‘s a little more complicated but runs faster. # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It‘s only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don‘t require it instead. # Get a reference to webcam #0 (the default one) video_capture = cv2.VideoCapture(0) # Load a sample picture and learn how to recognize it. obama_image = face_recognition.load_image_file("obama.jpg") obama_face_encoding = face_recognition.face_encodings(obama_image)[0] # Load a second sample picture and learn how to recognize it. biden_image = face_recognition.load_image_file("biden.jpg") biden_face_encoding = face_recognition.face_encodings(biden_image)[0] # Create arrays of known face encodings and their names known_face_encodings = [ obama_face_encoding, biden_face_encoding ] known_face_names = [ "Barack Obama", "Joe Biden" ] while True: # Grab a single frame of video ret, frame = video_capture.read() # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) rgb_frame = frame[:, :, ::-1] # Find all the faces and face enqcodings in the frame of video # face_locations = face_recognition.face_locations(rgb_frame) # face_encodings = face_recognition.face_encodings(rgb_frame, face_locations) face_landmarks_list = face_recognition.face_landmarks(rgb_frame) # Loop through each face in this frame of video # for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): # # See if the face is a match for the known face(s) # matches = face_recognition.compare_faces(known_face_encodings, face_encoding) # # name = "Unknown" # If a match was found in known_face_encodings, just use the first one. # if True in matches: # first_match_index = matches.index(True) # name = known_face_names[first_match_index] # # 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) for face_landmarks in face_landmarks_list: # Print the location of each facial feature in this image facial_features = [ ‘chin‘, ‘left_eyebrow‘, ‘right_eyebrow‘, ‘nose_bridge‘, ‘nose_tip‘, ‘left_eye‘, ‘right_eye‘, ‘top_lip‘, ‘bottom_lip‘ ] for facial_feature in facial_features: for point in face_landmarks[facial_feature]: cv2.circle(frame, point, 1, (255, 0, 0)) # Display the resulting image cv2.imshow(‘Video‘, frame) # Hit ‘q‘ on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord(‘q‘): break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows()