this is a demo of running face recognition on live video from your webcam it s a little more complicated than the other example but it includes some basic performance tweaks to make things run a lot faster 1 process each video frame at 14 resolution though still display it at full resolution 2 only detect faces in every other frame of video 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 initialize some variables grab a single frame of video only process every other frame of video to save time resize frame of video to 14 size for faster face recognition processing convert the image from bgr color which opencv uses to rgb color which facerecognition uses find all the faces and face encodings in the current 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 display the results scale back up face locations since the frame we detected in was scaled to 14 size 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 demo of running face recognition on live video from your webcam it s a little more complicated than the other example but it includes some basic performance tweaks to make things run a lot faster 1 process each video frame at 1 4 resolution though still display it at full resolution 2 only detect faces in every other frame of video 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 initialize some variables grab a single frame of video only process every other frame of video to save time resize frame of video to 1 4 size for faster face recognition processing convert the image from bgr color which opencv uses to rgb color which face_recognition uses find all the faces and face encodings in the current 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 display the results scale back up face locations since the frame we detected in was scaled to 1 4 size 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"
]


face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

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

    
    if process_this_frame:
        
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

        
        rgb_small_frame = small_frame[:, :, ::-1]
        
        
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in 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]

            face_names.append(name)

    process_this_frame = not process_this_frame


    
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        
        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()