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 facerecognition 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 load a sample picture and learn how to recognize it load a second sample picture and learn how to recognize it create arrays of known face encodings and their names grab a single frame of video convert the image from bgr color which opencv uses to rgb color which facerecognition uses find all the faces and face enqcodings in the frame of video loop through each face in this frame of video see if the face is a match for the known faces if a match was found in knownfaceencodings just use the first one if true in matches firstmatchindex matches indextrue name knownfacenamesfirstmatchindex or instead use the known face with the smallest distance to the new face draw a box around the face draw a label with a name below the face display the resulting image hit q on the keyboard to quit release handle to the webcam 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 load a sample picture and learn how to recognize it load a second sample picture and learn how to recognize it create arrays of known face encodings and their names grab a single frame of video convert the image from bgr color which opencv uses to rgb color which face_recognition uses find all the faces and face enqcodings in the frame of video loop through each face in this frame of video see if the face is a match for the known face s 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 or instead use the known face with the smallest distance to the new face draw a box around the face draw a label with a name below the face display the resulting image hit q on the keyboard to quit release handle to the webcam
import face_recognition
import cv2
import numpy as np









video_capture = cv2.VideoCapture(0)


obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]


biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]


known_face_encodings = [
    obama_face_encoding,
    biden_face_encoding
]
known_face_names = [
    "Barack Obama",
    "Joe Biden"
]

while True:
    
    ret, frame = video_capture.read()

    
    rgb_frame = frame[:, :, ::-1]

    
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

        name = "Unknown"

        
        
        
        

        
        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
        best_match_index = np.argmin(face_distances)
        if matches[best_match_index]:
            name = known_face_names[best_match_index]

        
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        
        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)

    
    cv2.imshow('Video', frame)

    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break


video_capture.release()
cv2.destroyAllWindows()