Quick face tracker using dlib + opencv. Face detector is slow, shape predictor is quick -- therefore they execute in separate threads.

dlib_face_tracker.py

#!/usr/bin/python3
import cv2 as cv
import dlib
from threading import Thread
import time

pose_model = dlib.shape_predictor("/usr/share/dlib/shape_predictor_68_face_landmarks.dat")
detector = dlib.get_frontal_face_detector()

def camera(source):
    cam = cv.VideoCapture(0)
    while 1:
        status, img = cam.read()
        if not status:
            return
        yield img

def points(r):
    return (r.left(), r.top()), (r.right(), r.bottom())

for img in camera(0):
    faces = detector(img)
    if not faces:
        continue
    face = faces[0]
    break

def print_fps(img):
    now = time.clock()
    if print_fps.last:
        cv.putText(img, f"fps: {1/(now - print_fps.last):.1f}", (10, 20), 0, 0.5, (0, 255, 255))
    print_fps.last = now
print_fps.last = None

def detect_forever():
    while detect_forever.img is None:
        # wait for initialization
        time.sleep(1)
    # the thread is shut down by setting img to None
    while detect_forever.img is not None:
        detect_forever.faces = detector(detect_forever.img)
        time.sleep(1)
detect_forever.img = None
detect_forever.faces = list()

t = Thread(target=detect_forever)
t.start()
for img in camera(0):
    detect_forever.img = img
    if not detect_forever.faces:
        # wait for initialization
        continue
    shape = pose_model(img, detect_forever.faces[0])
    horz = shape.part(13).x - shape.part(3).x
    vert = shape.part(8).y - shape.part(27).y
    face = dlib.rectangle(int(shape.part(3).x - 0.1*horz), int(shape.part(27).y - 0.4*vert), int(shape.part(13).x + 0.1 * horz), int(shape.part(8).y - 0.1*vert))
    cv.rectangle(img, *points(face), (0, 0, 255), 1)
    detect_forever.faces[0] = face
    print_fps(img)
    cv.imshow("face", img)
    if cv.waitKey(1) == 27:
        break
detect_forever.img = None