Computer Vision Applications using OpenCV

Facial Landmark Detection

Improve the speed of Facial Landmark Detection:

  1. Use face detection that comes with OS - if possible (e.g., Android, iOS)
  2. Skip frames - e.g,. run every 3 frames
  3. Downscale image, detect faces there … then propagate bboxes to original image (dividing the coordinates by the scale used for resizing the original frame.). Then just do landmark detection on original image and computed bounding box

Improve landmark stabilisation

  1. Weighted Moving Average - Average the landmark point location over a small time window
  2. Kalman Filtering
  3. Optical Flow

Code snippets

OpenCV - Rescale an image - keep ratio

import cv2

# Load image

img = cv2.imread("<path-to-img>")

# Scale down - keep ratio

dims = img.shape[0:2]
ratio = dims[0] / dims[1]

target_x = 960 # resolution - x
target_y = int(960 / ratio)
imgS = cv2.resize(img, (target_x, target_y))

cv2.namedWindow('windowname', cv2.WINDOW_NORMAL) # Create window with freedom of dimensions
cv2.imshow('windowname', imgS)
cv2.waitKey(0) # Display the image infinitely until any keypress

# in case you want to kill the window


OpenCV - imshow but keep it until pressed

import cv2
import time

def imshow\_(winname: str, img):
cv2.imshow(winname, img)

    while True:
        k = cv2.waitKey()
        if k == 27:


Hints & Tricks

  • dlib::faceDetector is trained on 80x80 faces. If the faces in your images are less than that then you need to upscale before running the detection.
  • dlib::landmarkDetector: Paper says it runs in 1ms
  • dlib -> RGB, OpenCV -> BGR. Thus for OpenCV -> dlib: cv2.cvtColor(img, cv2.COLOR_BGR2RGB)