Face Recognition using Convolutional Neural Network

 What is face recognition: A facial recognition system is a technology capable of matching a human face from a digital image or

Project Title

Face Recognition using Convolutional Neural Network

Project Area of Specialization

Artificial Intelligence

Project Summary

 What is face recognition:

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, typically employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image.

Problem:

Well, the technology we use in our project ( face recognition using the convolutional neural network) The reason is this We have to keep hire watchmen, Dedicated persons, Security forces, etc. inside a normal organization or in a company Who is checking the security pass and often there are long lines of people due to security purpose And a lot of time is lost in the pass So we are making a project to solve such a problem

So, we're going to have to figure out what we need to have a smart intelligence What will the camera do? The smart intelligence will automatically Detect human faces Every servant will go through the door through which he has installed the intelligence camera.

So, whoever comes over the door where the intelligence camera is installed so the intelligence mark attendance of an employee of a company or not. So, the person will be allowed if he is an employee of the company otherwise will not be allowed to enter the company.

Technology use:

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vision that includes image and video recognition

Project Objectives

Project Objectives

• We will ensure automatic attendance
• Security will be enhanced due to our project
• And more time will be saved
• And we will be studying existing work and we will try to promote it Meaning its accuracy  and speed performance and we will improve everything

Project Implementation Method

Implementation Method

Well now let's talk about project implementation We will complete our project in two ways First we will finish our software-related task Because I'm going to make software like that Which can anthemically process the faces which have been captured by using the camera.

And when our task of the software is completed Then we will start working on hardware work we will say that they will implement the build software on top of hardware meaning installing an intelligence camera, redberry pi same cabling, etc...

Software related task:

We will complete our software-related work in a few ways

1. Data collection:

We will do this project first for the university We have to collect the data of all the employees of the university From the IT data center.

1. Data Cleaning and Exploratory Data Analysis

When we have all the data So on top of that we will use some machine learning and deep learning techniques So that our data is perfectly clean and tidy. The reason for this is that we have performed some mathematical operations on top of this data. So, our deep learning model can easily predict human faces. When our data is completely ready where there is no noise and error data. So then we will implement our deep learning model on top of the preprocessed data.

1. Model building

We are using a deep learning model convolutional neural network. The neural network can be divided into two kinds, the biological neural network is one of them, and the artificial neural network is another kind. Here mainly introduces an artificial neural network. An artificial neural network is a data model that processes information and is similar in structure to the synaptic connections in the brain. A neural network is composed of many neurons; the output of the previous neuron can be used as the input of the latter neuron.

1. Training and hyperparameter tuning

We will train our model using a convolutional neural network a convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.

1. Testing

In the Testing phase, we will check the performance of our model whether it will  predict right or not

1. Deploying  Application

In the end, we will have a file That would be the model file trained by different images and the file will be deployed in hardware.

Hardware:

The hardware is composed center microcontroller ( Raspberry Pi 4 ) the camera and acheter will be attached to a raspberry pi. The camera will bring images to the live feed so our model is on the top of the microcontroller ( Raspberry Pi 4 ) where our model deployment will predict whether the person is legitimate or not. So, whether the person is an employee of the university will predict and acheter will start and the door will be open. if a person is not an employee of the university acheter will start beef sound.

Benefits of the Project

Benefits of the Project

We are currently building this project for a specific University, but we can also use them in many other places. But we can scale up  this application and use it in many places, For example, the police can use it To catch a criminal

• We will ensure automatic attendance
• Security will be enhanced due to our project
• And more time will be saved
• And we will be studying existing work and we will try to promote it Meaning its accuracy and speed performance and we will improve everything
• facial recognition can help identify terrorists or any other criminals with the help of the face scan only.
• security guards had to perform manual identification of a person which took too much time and did not boast high accuracy. But today, facial recognition is completely independent of the identification process and not only takes seconds but is also incredibly accurate.
• facial recognition to identify criminals with no other means of identification and find missing people by comparing faces on live camera feeds with those on a watch list.
• Facial recognition software can be an effective preemptive measure against shoplifting. Business owners use the software and security cameras to identify known or suspected thieves, and the presence of the cameras themselves works to deter theft in the first place.

Technical Details of Final Deliverable

Technical Details of Final Deliverable:

Our final project will be based on software and hardware. This is the reason we are demanding GPU because we have an intensive task of training our model using convolutional neural network So that they could have training on time and its needs much processing to predict good results. We will be using GPU for processing a lot of images that have been captured using a camera. And in the hardware, we will use the Raspberry Pi 4 model.

Equipment required

• Logitech Camera
• GPU card
• Raspberry Pi 4
• Miscellaneous

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Security

Other Industries

Education

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Quality Education

Required Resources

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