https://github.com/arbazkhan4712/face-recognition-door-lock-with-aws-rekognition-raspberry-pi3

Face Recognitio nDoor Lock with AWS Rekognition Raspberry Pi3
https://github.com/arbazkhan4712/face-recognition-door-lock-with-aws-rekognition-raspberry-pi3

aws awsrekognition face-detection face-recognition open opensource python raspberry-pi-camera

Last synced: 5 months ago
JSON representation

Face Recognitio nDoor Lock with AWS Rekognition Raspberry Pi3

• Host: GitHub
• URL: https://github.com/arbazkhan4712/face-recognition-door-lock-with-aws-rekognition-raspberry-pi3
• Owner: Arbazkhan4712
• License: mit
• Created: 2020-04-05T14:26:52.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-05-07T17:59:14.000Z (about 5 years ago)
• Last Synced: 2025-02-01T12:44:09.265Z (5 months ago)
• Topics: aws, awsrekognition, face-detection, face-recognition, open, opensource, python, raspberry-pi-camera
• Language: Python
• Size: 332 KB
• Stars: 14
• Watchers: 4
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Face-Recognition-Door-Lock-with-AWS-Rekognition-Raspberry-Pi3

Face Recognition Door Lock with AWS Rekognition & Raspberry Pi3 it works with RPI3 using the camera module

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                  [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)          [![PyPI license](https://img.shields.io/pypi/l/ansicolortags.svg)](https://pypi.python.org/pypi/ansicolortags/)

 [![GitHub forks](https://img.shields.io/github/forks/arbazkhan4712/Face-Recognition-Door-Lock-with-AWS-Rekognition-Raspberry-Pi3?style=social)](https://GitHub.com/Naereen/StrapDown.js/network/)                 [![GitHub star](https://img.shields.io/github/stars/arbazkhan4712/Face-Recognition-Door-Lock-with-AWS-Rekognition-Raspberry-Pi3?style=social)](https://GitHub.com/Naereen/StrapDown.js/network/)

## Video 

[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/mD8JOTDlbXE/0.jpg)](https://www.youtube.com/watch?v=mD8JOTDlbXE)

## Hardware Requirements:

#### 1.Raspberry Pi (Any Version Will Work)



#### 2.Raspberry Pi Camera (Also USB Webcam Can Be Used)



#### 3.Push Button



#### 4.Electric Door Lock



## Software Dependencies:

*Python2*

*Boto3*

```python

pip install boto3

```

*Python3*

*Boto3*

```python

pip3 install boto3

```

## Step 1 : Create a AWS S3 Bucket in that bucket create folders with the name of the students and add their images atleat 5-10

## Step 2 : Go to IAM and create a new user and set access type to Programmatic access 

## Step 3 : Set permissions for S3 and Rekoognition to full access 

## Step 4 : Complete the process you will get Accesss Key ID & Secret Access Key Copy both and add it in train.py and main.py

*train.py*

```

s3_client = boto3.client(

    's3',

    aws_access_key_id='',# add the aws access key

    aws_secret_access_key=''# add the aws secret access key

    

)

collectionId='' #collection name

rek_client=boto3.client('rekognition',

                            aws_access_key_id='',# add the aws access key

                            aws_secret_access_key='',# add the aws secret access key

                            region_name='',)# add the region here

```

*recognition.py*

```

rek_client=boto3.client('rekognition',

                        aws_access_key_id='',# add the aws access key

                        aws_secret_access_key='',# add the aws secret access key

                        region_name='ap-south-1',)# add the region here

```

## Step 5 : Add the S3 Bucket Name & Folder to save the images on pi

*Both files* 

```

bucket = '' #S3 bucket name

```

*recognition.py*

```

directory = '' #folder name on your raspberry pi

```

## Run

*First Run Train.py File on RPI*

```

python train.py

```

*Run main.py File on RPI ,connect the booton with GPIO 26* 

```

python main.py

```

## License & Copyright

© [Arbaz Khan](https://arbazkhan4712.github.io/Contact.html)

Licensed under the [MIT License](LICENSE)