Facial Recognition System

A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Researchers are currently developing multiple methods in which facial recognition systems work. The most advanced face recognition method, which is also emp

Project Title

Facial Recognition System

Project Area of Specialization

Artificial Intelligence

Project Summary

A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Researchers are currently developing multiple methods in which facial recognition systems work. The most advanced face recognition method, which is also employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image. Facial recognition systems analyse the visual data and millions of images and videos created by high-quality Closed-Circuit Television (CCTV) cameras installed in our cities for security, smartphones, social media, and other online activity.

Project Objectives

1. Our system will detect the face with mask, detect Smoking, Checkout proper Uniform.
2. We will connect our sensors with cameras and then we will start coding via R language and Python
3. We create the database with pictures (diiferent angles) we will link the database with our software so we can easily recognize the faces
4. We will apply machine learning to detect the faces angles and cuts (faces with mask and with glasses)

Project Implementation Method

It requires proper techniques for face detection and recognition with challenges of different facial expressions, pose variations, occlusion, aging and resolution either in the frame of stationary object or video sequencing images. And the other challenge is if someone is wearing mask or something like helmet or other things (Hijab) which cover their face then our retina recognition sensor feature will scan their eyes and will tell us that who is this person.

1. Design Phase: Hybrid face recognition systems use a combination of both holistic and feature extraction methods. Generally 3D Images are used in hybrid methods. The image of a person's face is caught in 3D, allowing the system to note the curves of the eye sockets, for example, or the shapes of the chin or forehead and the system will recognize face with mask by detecting the person's eye
2. Implementation phase: In application of the face recognition system EmguCV a wrapper library for OpenCV for .Net environment is utilized. MsSql server is used to store the reference image files. An Application with a user is developed. General methodology of test steps are listed below: ? Image to be compared is loaded to the application interface ? Face in the image is found by the algorithms described above and face is cut from the image ? Grey scale of cut face image is obtained ? It is compared (kNN) with the test set. For matching the grey scale face cut image Eigen face, Fisher face, LBPHface and SURF methods are use
3. Testing phase: ? Our domain is implement the project at university ? Our software is implemented at server room and linked with university’s database ? Sensors in cameras are connected with raspberry pi so it can fill our required solution ? If someone is wearing mask or hijab our system will recognize it through retinal sensors ? If someone is wearing sunglasses it will recognize it through bottom of face or recognize it through the picture in database. ? If someone is not in our database then the sensors detect and inform the surveillance room
4. Evaluation phase: We will test our system through an unknown person entering in our university’s premises the sensor will notify us.

Benefits of the Project

Face Detection: Find human faces in photos and images. 

Face identification: Search for face matches. Answers: “Who is this?”

Face verification: Search for someone's face. Answers: “Is this zahra?

Age detection: Detects age groups; child, young-adult, adult, or senior.

Multi-face detection: Detects individuals, crowds, audiences and groups

Facial coordinates: Detects size; pitch, roll, yaw, and key landmarks.

Diversity recognition: Understand more about the diversity of the human face.

Technical Details of Final Deliverable

An AI based Facial Recognition System , Which will detect any unusual activity in our university campus ,because our first platfrom for implmeneting this system is our own university campus.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Security

Other Industries

IT , Others , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Good Health and Well-Being for People, Gender Equality, Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Peace and Justice Strong Institutions

Required Resources

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