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Object detection and tracking are important in many computer vision applications, including activity recognition, automotive safety and surveillance. Presented here is an face detection using MATLAB system that can detect not only a human face but also eyes and upper body.Face detection is an easy and simple task for humans, but not so for computers. It has been regarded as the most complex and challenging problem in the field of computer vision due to large intra-class variations caused by the changes in facial appearance, lighting and expression. Such variations result in the face distribution to be highly nonlinear and complex in any space that is linear to the original image space.Face detection is the process of identifying one or more human faces in images or videos. It plays an important part in many biometric, security and surveillance systems, as well as image and video indexing systems.This face detection using program can be used to detect a face, eyes and upper body on pressing the corresponding buttons. The program output screen is shown in Fig. 1: Program output screen Software program.
Define and set-up your cascade object detector using the constructor:detector=vision.CascadeObjectDetectorIt creates a system object detector that detects objects using Viola-Jones algorithm. Its classification model property controls the type of object to detect. By default, the detector is configured to detect faces. 4: Face detection Fig.
5: Eyes detection Fig. 6: Upper body detection Download Source Code for the face detection using MATLAB project:2. Call the step method with input image I, cascade object detector, points PTS and any other optional properties.Below is the syntax for using the step method. Use the step syntax with input image I, selected cascade object detector and other optional properties to perform detection.BBOX = step (detector, I)It returns BBOX, an M-by-4 matrix defining M-bounding boxes, containing detected objects. This method performs multi-scale object detection on input image I. Each row of output matrix BBOX contains a four-element vector (x, y, width and height) that specifies in pixels, the upper-left corner and size of a bounding box. Input image I must be a gray scale or true colour (RGB) image.3.
The third step is:insertObjectAnnotation(I,’rectangle’,Position,Label)It inserts rectangles and corresponding labels at the location indicated by the position matrix. The position input must be an M-by-4 matrix, where each row (M) specifies a rectangle as a four-element vector (x, y, width and height). Elements x and y indicate the upper-left corner of the rectangle, and the width and height specify the size. Face detection using MATLAB system testingTo test this program, follow the steps given below:.
Install MATLAB version R2012a or higher version in your system. Launch it from desktop shortcut. You will see a blank command window. Washington drivers test parallel parking. Download the source folder.
Check the device ID, as shown in Fig. 2, and write the device ID number in the source code. Run the program. A GUI will appear, as shown in Fig.
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1. Click on Start button to initialise camera settings. Next, click on Face button and the camera will detect the face. Your face will be detected and displayed on the right side of the screen (Fig. Similarly, you can also detect your eyes and upper body by clicking on the respective buttons.
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The output screenshots for the detected eyes and upper body are shown in Fig. 6, respectively. Remember to click Stop button first to stop the previous process in order to detect other two features. To stop, click Stop button.The author is a B.Tech in electronics and communication from SRCEM, Gwalior. More MATLAB projects available. The article was first published on 20 April 2016 and was updated on 30 May 2019.
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