INTELLIGENT FACE DETECTION SYSTEM USING RASPBERY PI

Face recognition is a fast growing and challenging area in the field of computer vision and real time applications. A lot of techniques and algorithms are available with varying degrees of accuracy and speed. Face recognition has a lot of applications in the field of advertising, healthcare, securit

Project Title

INTELLIGENT FACE DETECTION SYSTEM USING RASPBERY PI

Project Area of Specialization

Internet of Things

Project Summary

Face recognition is a fast growing and challenging area in the field of computer vision and real time applications. A lot of techniques and algorithms are available with varying degrees of accuracy and speed. Face recognition has a lot of applications in the field of advertising, healthcare, security, accessibility, and even payments. Hence, there is a need for low cost, reliable and accurate face recognition systems in today’s world.

The aim is to implement a face recognition system using a Raspberry Pi device. This system is part of an assistive device created by us for visually impaired people. The setup consists of a Raspberry Pi 3 Model B device with a camera module attached to it. The Raspberry Pi has a 1.2 GHz 64-bit CPU along with 1 GB RAM and the camera module has a resolution of 5 MP.

One of the main challenges in face recognition is feature selection. In fact, it is a global optimization problem in machine learning. It is used to remove the number of features and irrelevant, noisy, redundant data in order to improve efficiency and accuracy. Methods based on genetic algorithms have been proposed which help to optimize the search strategies for feature selection. This can be particularly useful in real time applications. They have been used in tandem with some other techniques like Principal Component Analysis and Discrete Cosine Transform to achieve up to 99% accuracy in face detection.

Project Objectives

The system can be used in several places like banks, hospitals, labs and other sophisticated automated systems, which dramatically reduce the hazard of unauthorized entry. Evidence can be given to the security department if any robbery issue occurs. The design of the face recognition system using Raspberry pi can make the smaller, lighter and with lower power consumption, so it is more convenient than the PC-based face recognition system. Because of the open source code, it is free to do software development on Linux. The system was programmed using Python programming language. Both Real time face detection and face detection from specific images, i.e. object recognition, was carried out. The efficiency of the system was analysed in terms of face detection rate. The analysis revealed that the present system shows excellent performance efficiency and can be used for face detection even from poor quality images.

Project Implementation Method

1. OPEN CV METHOD 

 To perform the face recognition function, face detection is first performed to determine the position of the face in the picture. The Open CV method is a common method in face detection. It firstly extracts the feature images into a large sample set by extracting the face  features in the image and then uses the Ada Boost algorithm as the face detector. In face detection, the algorithm can effectively adapt to complex environments such as insufficient illumination and background blur, which greatly improves the accuracy of detection

     2.  IMAGE PROCESSING                                                                                   

Generally, multiple cameras were required to complete detailed automatic detection. And due to its complexity, specialized vision experts were often required to design, integrate, and install the system. These factors naturally limit it to certain large companies, but it is obviously inappropriate for small- and medium-sized companies that require a detection system. In contrast, the visual sensor is much simpler, compact, and easier to install and operate, making it more suitable for the needs of general enterprises.

        3  DATA EXTRACTION

1. Detection of human face  
2. Detection of eyes  
3. Framing the detected face  
4. Tracking the detected face and eye ( the frame moves with the face)  
5. Checking the detected face that it is not recorded before.
6. Preventing the person from logging in when the matching record is not found.  
7. Captioning the circled face when the matching record is found.
8. Letting the person recognized by the system log in.

Benefits of the Project

1. Automated Time Tracking System

Entry and exit time monitoring done manually or with other biometric systems can be fully automated with facial recognition attendance systems. There is no need for human intervention or physical validation as the system’s advanced algorithms can locate and identify faces autonomously. It is effortless to track time for employees with facial recognition.

2. Cost-Effective

Face detection  can save business resources by automatic employee time tracking. A solution like True in can be used on devices making it more affordable for small-scale and medium businesses. Irrespective of the business size, such an attendance system.Increase employee productivity by 10%

• Cut administrative costs by 5-10%
• Save 15% supervision time, helping supervisors with attendance control

3. More Accurate and Better Performance

Industrial floor time frauds are common worldwide and one of the most common work ethics violations. While a vast majority of workers are honest, but the nuisance of cannot be ruled out. Teaming up with staff members or security personnel, some workers skip work and still get paid. Such time fraud is not only detrimental to companies but is also unfair towards honest contributing workers.

4. Easy To Manage

As compared to manual attendance systems, AI-based attendance systems are highly automated. These systems store and update day-to-day records in real-time. From maintaining daily attendance to preparing high-accurate timesheets of individual employees, facial recognition attendance systems are programmed to handle it all on a very large scale. Imagine handling a large crowd of 10,000 people without any fuss and recording the attendance in an organized manner. Such is the efficiency of AI facial recognition systems.

5. Smart Integration

Integrating a face recognition system with any other HRMS or Payroll system is quite easy. As these systems are modular and highly customizable, the time-in time-out and date formats can be customized to be compatible with other systems implemented in an organization. It makes organizing data a lot easier. Also, the time zone settings can be easily changed as per geo-location that making it possible to use software worldwide without any additional requirement.

Technical Details of Final Deliverable

This system has historically worked like other form of  ''identification '' like face detect , eye detect , 

• Cut administrative costs by 5-10%
• Save 15% supervision time, helping supervisors with attendance control

 It is used to remove the number of features and irrelevant, noisy, redundant data in order to improve efficiency and accuracy. Methods based on genetic algorithms have been proposed which help to optimize the search strategies for feature selection. This can be particularly useful in real time applications. They have been used in tandem with some other techniques like Principal Component Analysis and Discrete Cosine Transform to achieve up to 99% accuracy in face detection.

Final Deliverable of the Project

Hardware System

Core Industry

Education

Other Industries

Security

Core Technology

Internet of Things (IoT)

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Quality Education

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com