This repository compare a selfie with images from identity documents and response if the selfie match.

Related tags

Deep Learningaws-rekognition-facecompare
Overview

aws-rekognition-facecompare

This repository compare a selfie with images from identity documents and response if the selfie match.

This code was made in a Python Notebook under SageMaker.

Set up:

  • Create a Notebook Instance in SageMaker
  • Notebook instance type : ml.t2.medium
  • Volume Size : 5GB EBS
  • Create a role for SageMaker with the following policies:
  • AmazonS3FullAccess
  • AmazonRekognitionFullAccess
  • AmazonSageMakerFullAccess
  1. Create a S3 Bucket
  2. Inside bucket create folder to insert the dataset images

Code Explanation

boto3 is needed to use the aws client of S3 and Rekognition. Just like what we do with variables, data can be kept as bytes in an in-memory buffer when we use the io module’s Byte IO operations, so we can load images froms S3. At least Pillow is needed for image plotting.

import boto3
import io
from PIL import Image, ImageDraw, ExifTags, ImageColor

rekognition_client=boto3.client('rekognition')
s3_resource = boto3.resource('s3')

In this notebook I use two functions of AWS Rekognition

  • detect_faces : Detect faces in the image. It also evaluate different metrics and create different landmarks for all elements of the face like eyes positions.
  • compare_faces : Evaluate the similarity of two faces.

Case of use

Here I explain how to compare two images

The compare function

IMG_SOURCE ="dataset-CI/imgsource.jpg"
IMG_TARGET ="dataset-CI/img20.jpg"
response = rekognition_client.compare_faces(
                SourceImage={
                    'S3Object': {
                        'Bucket': BUCKET,
                        'Name': IMG_SOURCE
                    }
                },
                TargetImage={
                    'S3Object': {
                        'Bucket': BUCKET,
                        'Name': IMG_TARGET                    
                    }
                }
)

response

{'SourceImageFace': {'BoundingBox': {'Width': 0.3676206171512604,
   'Height': 0.5122320055961609,
   'Left': 0.33957839012145996,
   'Top': 0.18869829177856445},
  'Confidence': 99.99957275390625},
 'FaceMatches': [{'Similarity': 99.99634552001953,
   'Face': {'BoundingBox': {'Width': 0.14619407057762146,
     'Height': 0.26241832971572876,
     'Left': 0.13103649020195007,
     'Top': 0.40437373518943787},
    'Confidence': 99.99955749511719,
    'Landmarks': [{'Type': 'eyeLeft',
      'X': 0.17260463535785675,
      'Y': 0.5030772089958191},
     {'Type': 'eyeRight', 'X': 0.23902645707130432, 'Y': 0.5023221969604492},
     {'Type': 'mouthLeft', 'X': 0.17937719821929932, 'Y': 0.5977044105529785},
     {'Type': 'mouthRight', 'X': 0.23477530479431152, 'Y': 0.5970458984375},
     {'Type': 'nose', 'X': 0.20820103585720062, 'Y': 0.5500822067260742}],
    'Pose': {'Roll': 0.4675966203212738,
     'Yaw': 1.592366099357605,
     'Pitch': 8.6331205368042},
    'Quality': {'Brightness': 85.35185241699219,
     'Sharpness': 89.85481262207031}}}],
 'UnmatchedFaces': [],
 'ResponseMetadata': {'RequestId': '3ae9032d-de8a-41ef-b22f-f95c70eed783',
  'HTTPStatusCode': 200,
  'HTTPHeaders': {'x-amzn-requestid': '3ae9032d-de8a-41ef-b22f-f95c70eed783',
   'content-type': 'application/x-amz-json-1.1',
   'content-length': '911',
   'date': 'Wed, 26 Jan 2022 17:21:53 GMT'},
  'RetryAttempts': 0}}

If the source image match with the target image, the json return a key "FaceMatches" with a non-empty, otherwise it returns a key "UnmatchedFaces" with a non-empty array.

# Analisis imagen source
s3_object = s3_resource.Object(BUCKET,IMG_SOURCE)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
imgWidth, imgHeight = image.size  
draw = ImageDraw.Draw(image)  

box = response['SourceImageFace']['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']

print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Face Width: ' + "{0:.0f}".format(width))
print('Face Height: ' + "{0:.0f}".format(height))

points = (
    (left,top),
    (left + width, top),
    (left + width, top + height),
    (left , top + height),
    (left, top)

)
draw.line(points, fill='#00d400', width=2)

image.show()
Left: 217
Top: 121
Face Width: 235
Face Height: 328

png

0: for face in response['FaceMatches']: face_match = face['Face'] box = face_match['BoundingBox'] left = imgWidth * box['Left'] top = imgHeight * box['Top'] width = imgWidth * box['Width'] height = imgHeight * box['Height'] print('FaceMatches') print('Left: ' + '{0:.0f}'.format(left)) print('Top: ' + '{0:.0f}'.format(top)) print('Face Width: ' + "{0:.0f}".format(width)) print('Face Height: ' + "{0:.0f}".format(height)) points = ( (left,top), (left + width, top), (left + width, top + height), (left , top + height), (left, top) ) draw.line(points, fill='#00d400', width=2) image.show()"> 
# Analisis imagen target
s3_object = s3_resource.Object(BUCKET,IMG_TARGET)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
imgWidth, imgHeight = image.size  
draw = ImageDraw.Draw(image)
if len(response['UnmatchedFaces']) > 0:
    for face in response['UnmatchedFaces']:
        box = face['BoundingBox']
        left = imgWidth * box['Left']
        top = imgHeight * box['Top']
        width = imgWidth * box['Width']
        height = imgHeight * box['Height']
        print('UnmatchedFaces')
        print('Left: ' + '{0:.0f}'.format(left))
        print('Top: ' + '{0:.0f}'.format(top))
        print('Face Width: ' + "{0:.0f}".format(width))
        print('Face Height: ' + "{0:.0f}".format(height))

        points = (
            (left,top),
            (left + width, top),
            (left + width, top + height),
            (left , top + height),
            (left, top)

        )
        draw.line(points, fill='#ff0000', width=2)
        
if len(response['FaceMatches']) > 0:
    for face in response['FaceMatches']:
        face_match = face['Face']
        box = face_match['BoundingBox']
        left = imgWidth * box['Left']
        top = imgHeight * box['Top']
        width = imgWidth * box['Width']
        height = imgHeight * box['Height']
        print('FaceMatches')
        print('Left: ' + '{0:.0f}'.format(left))
        print('Top: ' + '{0:.0f}'.format(top))
        print('Face Width: ' + "{0:.0f}".format(width))
        print('Face Height: ' + "{0:.0f}".format(height))

        points = (
            (left,top),
            (left + width, top),
            (left + width, top + height),
            (left , top + height),
            (left, top)

        )
        draw.line(points, fill='#00d400', width=2)        
image.show()
FaceMatches
Left: 671
Top: 1553
Face Width: 749
Face Height: 1008

png

Owner
GitHub Repository
Predicting future trajectories of people in cameras of novel scenarios and views.

Pedestrian Trajectory Prediction Predicting future trajectories of pedestrians in cameras of novel scenarios and views. This repository contains the c

8 Sep 03, 2022
AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation

AirPose AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation Check the teaser video This repository contains the code of A

Robot Perception Group 41 Dec 05, 2022
Recursive Bayesian Networks

Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi

Robert Lieck 11 Oct 18, 2022
Worktory is a python library created with the single purpose of simplifying the inventory management of network automation scripts.

Worktory is a python library created with the single purpose of simplifying the inventory management of network automation scripts.

Renato Almeida de Oliveira 18 Aug 31, 2022
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

2 Sep 22, 2022
SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss

"# SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING" i

Saeed Lotfi 28 Dec 12, 2022
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
Light-Head R-CNN

Light-head R-CNN Introduction We release code for Light-Head R-CNN. This is my best practice for my research. This repo is organized as follows: light

jemmy li 835 Dec 06, 2022
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Bitcoin Realized Volatility Forecasting with GARCH and Multivariate LSTM Author: Chi Bui This Repository Repository Directory ├── README.md

Chi Bui 113 Dec 29, 2022
An implementation on "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance"

Lidar-Segementation An implementation on "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance" from

Wangxu1996 135 Jan 06, 2023
Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe

Traductor de señas Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe Requerimientos 🔧 Python 3.8 o inferior para evitar

Jahaziel Hernandez Hoyos 3 Nov 12, 2022
load .txt to train YOLOX, same as Yolo others

YOLOX train your data you need generate data.txt like follow format (per line- one image). prepare one data.txt like this: img_path1 x1,y1,x2,y2,clas

LiMingf 18 Aug 18, 2022
Few-Shot Object Detection via Association and DIscrimination

Few-Shot Object Detection via Association and DIscrimination Code release of our NeurIPS 2021 paper: Few-Shot Object Detection via Association and DIs

Cao Yuhang 49 Dec 18, 2022
This is a Keras implementation of a CNN for estimating age, gender and mask from a camera.

face-detector-age-gender This is a Keras implementation of a CNN for estimating age, gender and mask from a camera. Before run face detector app, expr

Devdreamsolution 2 Dec 04, 2021
AI that generate music

PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia

Annas 28 Nov 27, 2022
Pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model'

RTK-PAD This is an official pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model', which is accepted by IEEE T

6 Aug 01, 2022
🛰️ List of earth observation companies and job sites

Earth Observation Companies & Jobs source Portals & Jobs Geospatial Geospatial jobs newsletter: ~biweekly newsletter with geospatial jobs by Ali Ahmad

Dahn 64 Dec 27, 2022
✂️ EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video.

EyeLipCropper EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video. The whole process consists of three parts: frame extracti

Zi-Han Liu 9 Oct 25, 2022
The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection .

GCoNet The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection . Trained model Download final_gconet.pth

Qi Fan 46 Nov 17, 2022
A faster pytorch implementation of faster r-cnn

A Faster Pytorch Implementation of Faster R-CNN Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the firs

Jianwei Yang 7.1k Jan 01, 2023