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

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