Intelligent Verification of Covid Certificate using Face Recognition System
In recent years face recognition has received substantial attention from researchers in biometrics, pattern recognition, and computer vision communities. The machine learning and computer graphics communities are also increasingly involved in face recognition. Besides, there are a large number of co
2025-06-28 16:28:00 - Adil Khan
Intelligent Verification of Covid Certificate using Face Recognition System
Project Area of Specialization Artificial IntelligenceProject SummaryIn recent years face recognition has received substantial attention from researchers in biometrics, pattern recognition, and computer vision communities. The machine learning and computer graphics communities are also increasingly involved in face recognition. Besides, there are a large number of commercial, securities, and forensic applications requiring the use of face recognition technologies. Face recognition has attracted much attention and its research has rapidly expanded by not only engineers but also neuroscientists, since it has many potential applications in computer vision communication and automatic access control system. Especially, face detection is an important part of face recognition
There are several techniques in machine learning to detect and recognize face. Human face consists of multidimensional structure and required a quality computing technique for recognition. To identify a face in images, there are several things to look as a pattern, such as height, color of the faces, width of other parts of the face like lips, nose, eyes, etc. Clearly, there is a pattern, different faces have different dimensions, and similar faces have similar dimensions. We have to convert a particular face into numbers.
Identifying a person with an image has been popularized through the mass media. However, it is less robust to fingerprint or retina scanning. Facial Recognition represents the event of a system which may determine the person with the assistance of a face using Computer Vision (Open CV). Face recognition is employed within the fields of Identity Recognition, police investigation and enforcement. It's a method of characteristic someone supported facial expression.
In our project, we will use deep learning to recognize a face. Our main aim is not only face recognition but also, we want to verify either a person is Covid vaccinated or not on the behave of certificate.
Project Objectives- The main objective of this work is to make the certificate verification system efficient, time saving, simple and easy. Compared to traditional certificate verification system, this system reduces the workload of people.
- As the technology becomes more widespread, customers will be able to pay in stores using their face, rather than pulling out their credit cards or cash. This could save time in checkout lines. Since there is no contact required for facial recognition as there is with fingerprinting or other security measures – useful in the post-COVID world – facial recognition offers a quick, automatic, and seamless verification experience.
- The process of recognizing a face takes only a second, which has benefits for the companies that use facial recognition. In an era of cyber-attacks and advanced hacking tools, companies need both secure and fast technologies. Facial recognition enables quick and efficient verification of a person’s identity.
- Public concern over unjustified stops and searches is a source of controversy for the police — facial recognition technology could improve the process. By singling out suspects among crowds through an automated rather than human process, face recognition technology could help reduce potential bias and decrease stops and searches on law-abiding citizens.
- Most facial recognition solutions are compatible with most security software. In fact, it is easily integrated. This limits the amount of additional investment required to implement it.
Face recognition is the task of identifying an already detected object as a known or unknown face. Often the problem of face recognition is confused with the problem of face detection Face Recognition on the other hand is to decide if the "face" is someone known, or unknown, using for this purpose a database of faces in order to validate this input face
FACE RECOGNIZATION:
DIFFERENT APPROACHES OF FACE RECOGNITION:
There are two predominant approaches to the face recognition problem: Geometric (feature based) and photometric (view based). As researcher interest in face recognition continued, many different algorithms were developed, three of which have been well studied in face recognition literature. Recognition algorithms can be divided into two main approaches:
1. Geometric: Is based on geometrical relationship between facial landmarks, or in other words the spatial configuration of facial features. That means that the main geometrical features of the face such as the eyes, nose and mouth are first located and then faces are classified on the basis of various geometrical distances and angles between features
2. Photometric stereo: Used to recover the shape of an object from a number of images taken under different lighting conditions. The shape of the recovered object is defined by a gradient map, which is made up of an array of surface normal (Zhao and Chellappa, 2006)
Popular recognition algorithms include:
- Principal Component Analysis using Eigenfaces, (PCA)
- Linear Discriminate Analysis,
- Elastic Bunch Graph Matching using the Fisherface algorithm
FACE DETECTION:
Face detection involves separating image windows into two classes; one containing faces (training the background (clutter). It is difficult because although commonalities exist between faces, they can vary considerably in terms of age, skin color and facial expression. The problem is further complicated by differing lighting conditions, image qualities and geometries, as well as the possibility of partial occlusion and disguise. An ideal face detector would therefore be able to detect the presence of any face under any set of lighting conditions, upon any background. The face detection task can be broken down into two steps. The first step is a classification task that takes some arbitrary image as input and outputs a binary value of yes or no, indicating whether there are any faces present in the image. The second step is the face localization task that aims to take an image as input and output the location of any face or faces within that image as some bounding box with (x, y, width, height).
Benefits of the Project- Provide safety for screening person at entrance
- Decreases the spread of Covid virus
- Foolproof
- Time saving
- Efficient
- It offers protection against intruders as they can be easily detected.
- It can be faster than other biometrics authentication methods.
- Footage can be recorded to be manually checked.
These sections deal primarily with proposed techniques, methodologies and concepts relevant to facial recognition and image processing which is more specific and niche to a single process which uses facial recognition algorithms image processing techniques. The proposed project includes four sequential phases; capture ,detection, image matching and certificate verification .
- Primary database creation and training
- Image Capturing Phase
- Image Detection Phase
- Image Matching Phase
- Certificate verification Phase
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 70000 | |||
| CCTV Cameras | Miscellaneous | 1 | 5000 | 5000 |
| TRDB-D5M | Equipment | 1 | 15000 | 15000 |
| DE10-Nano Cyclone V SE SoC Development Kit | Equipment | 1 | 30000 | 30000 |
| OpenCV AI Kit | Equipment | 1 | 20000 | 20000 |