#Eye timer computer how to#
#Eye timer computer code#

In our next section, we’ll learn how to implement the eye aspect ratio for blink detection using facial landmarks, OpenCV, Python, and dlib. As we can see, the eye aspect ratio is constant, then rapidly drops close to zero, then increases again, indicating a single blink has taken place. The bottom figure plots a graph of the eye aspect ratio over time for a video clip. However, once the person blinks ( top-right) the eye aspect ratio decreases dramatically, approaching zero. On the top-left we have an eye that is fully open - the eye aspect ratio here would be large(r) and relatively constant over time. The dip in the eye aspect ratio indicates a blink (Figure 1 of Soukupová and Čech). Bottom: Plotting the eye aspect ratio over time. Top-right: Eye landmarks when the eye is closed. To make this more clear, consider the following figure from Soukupová and Čech: Figure 5: Top-left: A visualization of eye landmarks when then the eye is open. Using this simple equation, we can avoid image processing techniques and simply rely on the ratio of eye landmark distances to determine if a person is blinking. Well, as we’ll find out, the eye aspect ratio is approximately constant while the eye is open, but will rapidly fall to zero when a blink is taking place. The numerator of this equation computes the distance between the vertical eye landmarks while the denominator computes the distance between horizontal eye landmarks, weighting the denominator appropriately since there is only one set of horizontal points but two sets of vertical points. Where p1, …, p6 are 2D facial landmark locations. There is a relation between the width and the height of these coordinates.īased on the work by Soukupová and Čech in their 2016 paper, Real-Time Eye Blink Detection using Facial Landmarks, we can then derive an equation that reflects this relation called the eye aspect ratio (EAR): Figure 4: The eye aspect ratio equation. In terms of blink detection, we are only interested in two sets of facial structures - the eyes.Įach eye is represented by 6 (x, y)-coordinates, starting at the left-corner of the eye (as if you were looking at the person), and then working clockwise around the remainder of the region: Figure 3: The 6 facial landmarks associated with the eye.īased on this image, we should take away on key point: This also implies that we can extract specific facial structures by knowing the indexes of the particular face parts: Figure 2: Applying facial landmarks to localize various regions of the face, including eyes, eyebrows, nose, mouth, and jawline. 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.

#Eye timer computer 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. In last week’s blog post, I demonstrated how to perform facial landmark detection in real-time in video streams.