Understanding the “eye aspect ratio” (EAR)Īs we learned from our previous tutorial, we can apply facial landmark detection to localize important regions of the face, including eyes, eyebrows, nose, ears, and mouth: Figure 1: Detecting facial landmarks in an video stream in real-time.
#MY EYES ONLY PASSWORD CHANGE CODE#
In the first part we’ll discuss the eye aspect ratio and how it can be used to determine if a person is blinking or not in a given video frame.įrom there, we’ll write Python, OpenCV, and dlib code to (1) perform facial landmark detection and (2) detect blinks in video streams.īased on this implementation we’ll apply our method to detecting blinks in example webcam streams along with video files.įinally, I’ll wrap up today’s blog post by discussing methods to improve our blink detector. Our blink detection blog post is divided into four parts. Looking for the source code to this post? Jump Right To The Downloads Section Eye blink detection with OpenCV, Python, and dlib To learn more about building a computer vision system to detect blinks in video streams using OpenCV, Python, and dlib, just keep reading. This method for eye blink detection is fast, efficient, and easy to implement. The eye aspect ratio is instead a much more elegant solution that involves a very simple calculation based on the ratio of distances between facial landmarks of the eyes. Determining if the “white” region of the eyes disappears for a period of time (indicating a blink).Thresholding to find the whites of the eyes.Unlike traditional image processing methods for computing blinks which typically involve some combination of: To build our blink detector, we’ll be computing a metric called the eye aspect ratio (EAR), introduced by Soukupová and Čech in their 2016 paper, Real-Time Eye Blink Detection Using Facial Landmarks. Today, we are going to build upon this knowledge and develop a computer vision application that is capable of detecting and counting blinks in video streams using facial landmarks and OpenCV.
#MY EYES ONLY PASSWORD CHANGE HOW TO#
In last week’s blog post, I demonstrated how to perform facial landmark detection in real-time in video streams. Click here to download the source code to this post
0 kommentar(er)
