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

Last update: Jan 27, 2022

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

Create a S3 Bucket
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

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

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

Related tags

Overview

aws-rekognition-facecompare

Set up:

Code Explanation

Case of use

Owner

Commonsense Ability Tests

Face Transformer for Recognition

A facial recognition doorbell system using a Raspberry Pi

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

Simple-System-Convert--C--F - Simple System Convert With Python

Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai

Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

Use deep learning, genetic programming and other methods to predict stock and market movements

A visualization tool to show a TensorFlow's graph like TensorBoard

Txt2Xml tool will help you convert from txt COCO format to VOC xml format in Object Detection Problem.

Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Serverless proxy for Spark cluster

Code for GNMR in ICDE 2021

Research code for CVPR 2021 paper "End-to-End Human Pose and Mesh Reconstruction with Transformers"

3D Pose Estimation for Vehicles

University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN

Robustness via Cross-Domain Ensembles

DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation