I recently came across a post on Reddit titled “Fastest face tracking implementation I’ve ever seen.” by user ReadyThor. It inspired me to write a quick tutorial on how to implement fast and accurate face detection with python. The following code uses Dlib aåçnd OpenCV to detect faces in a live-webcam feed. A lot of face detection tutorials use OpenCV’s Haar cascades to detect faces. However, Haar cascades are old in Moore years. Dlib’s method is much more accurate. You can see the difference in this fun video. Another touch I implemented to make this tutorial different from the rest was to style the bounding boxes. So many tutorials use just the standard solid green bounding box. My program uses semi-transparent curved lines. I hope you find the tutorial helpful. If you have any questions feel free to ask them in the comments below. import dlib # dlib for accurate face detection import cv2 # opencv import imutils # helper functions from pyimagesearch.com # Grab video from your webcam stream = cv2.VideoCapture(0) # Face detector detector = dlib.get_frontal_face_detector() # Fancy box drawing function by Dan Masek def draw_border(img, pt1, pt2, color, thickness, r, d): x1, y1 = pt1 x2, y2 = pt2 # Top left drawing cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness) cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness) cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness) # Top right drawing cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness) cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness) cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness) # Bottom left drawing cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness) cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness) cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness) # Bottom right drawing cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness) cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness) cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness) while True: # read frames from live web cam stream (grabbed, frame) = stream.read() # resize the frames to be smaller and switch to gray scale frame = imutils.resize(frame, width=700) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Make copies of the frame for transparency processing overlay = frame.copy() output = frame.copy() # set transparency value alpha = 0.5 # detect faces in the gray scale frame face_rects = detector(gray, 0) # loop over the face detections for i, d in enumerate(face_rects): x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height() # draw a fancy border around the faces draw_border(overlay, (x1, y1), (x2, y2), (162, 255, 0), 2, 10, 10) # make semi-transparent bounding box cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output) # show the frame cv2.imshow("Face Detection", output) key = cv2.waitKey(1) & 0xFF # press q to break out of the loop if key == ord("q"): break # cleanup cv2.destroyAllWindows() stream.stop()