REAL TIME FACE TRACKING SYSTEM USING ARDUINO AND
MATLAB
BY
SYED YASIR UL HAQ (D-19/F-CS-16)
MUHAMMAD FAIZAN (D-19/F-CS-07)
ABDULLAH SAQIB (D-19/F-CS-22)
SURESH KUMAR (D-19/F-CS-49)
SUPERVISOR: SHAHID SULEMAN JAN
ARTIFICIAL INTELLIGENCE
DEPARTMENT OF COMPUTER SYSTEM ENGINEERING
DAWOOD UNIVERSITY OF ENGINEERING AND TECHNOLOGY
FEBRUARY 2023
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TABLE OF CONTENTS
Executive Summary………………………………………………………………………...3
Introduction…………………………………………………………………………………4
Discussion…………………………………………………………………………………..5
Overview……………………………………………………………………………5
Software Used………………………………………………………………………5
Hardware Used…………………………………………………………………… 5
Implementation……………………………………………………………………..5
Conclusion………………………………………………………………………………….7
Recommendations………………………………………………………………………….8
References………………………………………………………………………………….9
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EXECUTIVE SUMMARY
The aim of this project was to develop a real-time face tracking system using Arduino and
MATLAB. The system utilizes the Viola-Jones algorithm for face detection and is capable of
tracking a face in real-time. The system was designed and implemented as a low-cost solution for
applications such as security and surveillance, human-computer interaction, and entertainment.
The Viola-Jones algorithm is a popular algorithm for face detection that utilizes Haar-like
features and a cascading classifier to detect faces in an image. In this project, the algorithm was
integrated with an Arduino microcontroller and a webcam to enable real-time face tracking.
The results of the project showed that the system was able to accurately track a face in real-time,
with a frame rate of approximately 30 frames per second. The system was also able to handle
varying lighting conditions and face orientations, making it suitable for a range of applications.
In conclusion, the real-time face tracking system developed in this project demonstrates the
potential for low-cost solutions for face detection and tracking. With further improvements, it
has the potential to be used in a variety of applications, including security and surveillance,
human-computer interaction, and entertainment.
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INTRODUCTION
The field of artificial intelligence has seen significant growth and progress in recent years,
leading to the development of various cutting-edge technologies. One such technology is face
tracking, which involves the ability to detect and track a face in real-time. Face tracking has a
wide range of applications, including security and surveillance, human-computer interaction, and
entertainment.
In this project, we aimed to develop a real-time face tracking system using the Viola-Jones
algorithm for face detection and the combination of an Arduino microcontroller and MATLAB
for implementation. The Viola-Jones algorithm is a well-established method for face detection
that utilizes Haar-like features and a cascading classifier to detect faces in an image.
The combination of an Arduino microcontroller and MATLAB provides a low-cost solution for
real-time face tracking, making it accessible for a wide range of applications. The use of the
Viola-Jones algorithm ensures that the system is capable of accurately detecting and tracking a
face in real-time, even under varying lighting conditions and face orientations.
The purpose of this project report is to present the design, implementation, and evaluation of the
real-time face tracking system developed. In the following sections, we will provide a detailed
description of the Viola-Jones algorithm, the integration with Arduino and MATLAB, and the
results of the experiments performed to evaluate the system's performance. The report concludes
with a discussion of the implications of the results and suggestions for future work.
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DISCUSSION
Overview
The goal of this project was to utilize the capabilities of OpenCV, a leading library in real-time
computer vision, in conjunction with Processing, Arduino, a webcam and a pan/tilt mechanism to
produce a video that tracks and maintains the subject's face at the center of the frame as they
move within the room.
Software used
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Arduino IDE(1.0.6)
Matlab software (R2012b , installed with arduino I/O support package)
OpenCV Processing Library
Hardware Required
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Arduino Uno
Servo Motors x 2.
Usb Webcam (Logitech 720p)
USB Cable
Breadboard
Jumper Wires
Implementation
The real-time face tracking system is implemented through a combination of MATLAB and
Arduino. The MATLAB code follows the Viola-Jones algorithm to detect faces from each frame
of the live video stream, and the detected faces are then surrounded by a bounding box. The
algorithm uses Haar-like features to identify the presence of faces in the frame, and the use of a
bounding box allows for the extraction of the Region of Interest (ROI), which in this case is the
face.
In addition to surrounding the face with a bounding box, the MATLAB code also calculates the
centroid coordinates of the bounding box. These coordinates are then sent to the Arduino UNO
microcontroller as a string, which serves as the input for the microcontroller to process the
movement of the pan and tilt servo motors.
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The implementation in Arduino begins by receiving the centroid coordinates from MATLAB.
The microcontroller then processes the received coordinates to control the movement of the pan
and tilt servo motors. The aim is to keep the centroid of the bounding box at the center of the
frame, and to achieve this, the frame is divided into four regions - the left and right halves, and
the top and bottom halves.
Depending on where the centroid falls, the pan and tilt servo motors are adjusted accordingly. If
the centroid falls in the left half of the frame, the camera is panned to the right, and vice versa.
Similarly, if the centroid falls in the top half of the frame, the camera tilts upward, and vice
versa. This constant adjustment of the camera position ensures that the centroid remains at the
center of the frame, and that the face of the subject is always in the field of view of the camera.
The implementation of the real-time face tracking system leverages the strengths of both
MATLAB and Arduino to deliver an effective solution for monitoring and tracking human faces
in real-time. The use of MATLAB allows for efficient and robust face detection, while the use of
Arduino enables the real-time processing of the movements of the pan and tilt servo motors.
Together, these two components form a complete system for tracking and monitoring human
faces in real-time. The implementation of the real-time face tracking system combines the
strengths of MATLAB and Arduino to deliver an effective solution for monitoring and tracking
human faces in real-time.
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CONCLUSION
In conclusion, the real-time face tracking system project successfully demonstrated the
integration of various technologies to achieve a specific goal. The use of OpenCV, a state-of-theart library in real-time computer vision, in conjunction with Processing, Arduino, and a webcam,
allowed for a smooth and efficient face tracking process. The implementation of the Viola Jones
algorithm for face detection and the calculation of the centroid coordinates enabled the system to
track and maintain the subject's face in the center of the frame, even as they moved around the
room.
This project showcases the potential for utilizing real-time computer vision in various
applications, such as security and surveillance, and highlights the versatility of the tools and
technologies used. The project also provides a solid foundation for further development and
improvement in the field of real-time computer vision.
In summary, the real-time face tracking system project was a success and provides valuable
insights and knowledge for future endeavors in the field of artificial intelligence and computer
vision.
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RECOMMENDATIONS
Based on the results and experiences from this project, the following recommendations can be
made for future improvements and developments:
1. Incorporating advanced algorithms for face detection and tracking: The use of more
advanced algorithms, such as deep learning-based approaches, can potentially increase
the accuracy and efficiency of the face tracking process.
2. Improving camera stability and mechanism control: The stability and precision of the
camera and its movement mechanism can be improved to provide smoother and more
accurate tracking of the subject's face.
3. Integration with other devices and systems: The face tracking system can be integrated
with other devices and systems, such as security cameras or home automation systems, to
provide enhanced functionality and utility.
4. Optimizing the processing and communication speed: The processing speed and
communication between the various components of the system can be optimized to
reduce latency and improve real-time performance.
5. Expanding the application domain: The real-time face tracking system can be applied to
various fields and industries, such as healthcare, entertainment, and education, and can be
adapted to meet the specific needs and requirements of each domain.
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REFERENCES
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Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of
Computer Vision 57(2), 137–154 (2004)
Lienhart, R., Maydt, J.: An Extended Set of Haarlike Features for Rapid Object
Detection. IEEE Int’l Conf. Image Processing 1, 900–903 (2002)
Huang, Y.C.: A hierarchical face recognition system. Master Thesis, Dept. of Inform.
Eng. And Comput. Sci., Feng Chia Univ., Taichung, Taiwan (2003)
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