Facial Recognition Using Amazon Rekognition On Python

AWS offers a powerful facial recognition service called Amazon Rekognition. In this tutorial, we'll use Python and the AWS SDK to create a simple app that uses the Rekognition service to detect faces in an image.

To get started, you'll need an AWS account and access to the AWS Console. Once you're logged in, navigate to the Security Credentials in the right nav bar and create a user with the AmazonRekognitionFullAccess permission. Then, create the access key and copy the secret key as well.

Next, you'll need to install the AWS SDK for Python (Boto3) using the following command:

pip install boto3

Once you've installed Boto3, you can import the necessary libraries in your Python script:

import boto3
from PIL import Image, ImageDraw, ImageFont

To use the Rekognition service, you'll need to create a Boto3 client and specify the region:

rekognition = boto3.client('rekognition', aws_access_key_id='<YOUR_ACCESS_KEY_ID>', aws_secret_access_key='<YOUR_SECRET_ACCESS_KEY>', region_name='us-east-1')

For this example, we're going to use Arial font. You can use whatever you want. First, you must download the TTF file for that font.

font = ImageFont.truetype('arial.ttf', 30)

Now, we will read the image and call the detect_faces method.

with open(image_name, 'rb') as image_data:
        response_content = image_data.read()

rekognition_response = rekognition.detect_faces(Image={'Bytes': response_content}, Attributes=['ALL'])

This is the summarized response from the above request.

{
   "FaceDetails":[
      {
         "BoundingBox":{
            "Width":0.1575244814157486,
            "Height":0.26773321628570557,
            "Left":0.7044062614440918,
            "Top":0.35157522559165955
         },
         "AgeRange":{
            "Low":22,
            "High":30
         },
         "Smile":{
            "Value":true,
            "Confidence":96.33894348144531
         },
         "Eyeglasses":{
            "Value":false,
            "Confidence":97.71277618408203
         },
         "Sunglasses":{
            "Value":false,
            "Confidence":99.9967269897461
         },
         "Gender":{
            "Value":"Male",
            "Confidence":99.95927429199219
         },
         "Beard":{
            "Value":true,
            "Confidence":99.96766662597656
         },
         "Mustache":{
            "Value":true,
            "Confidence":88.09671783447266
         },
         "EyesOpen":{
            "Value":true,
            "Confidence":98.2445297241211
         },
         "MouthOpen":{
            "Value":true,
            "Confidence":95.4747314453125
         },
         "Emotions":[
            {
               "Type":"HAPPY",
               "Confidence":99.83971405029297
            },
            {
               "Type":"SURPRISED",
               "Confidence":6.266087532043457
            },
            {
               "Type":"FEAR",
               "Confidence":5.88023567199707
            },
            {
               "Type":"SAD",
               "Confidence":2.1496529579162598
            },
            {
               "Type":"CONFUSED",
               "Confidence":0.06452298164367676
            },
            {
               "Type":"CALM",
               "Confidence":0.018650680780410767
            },
            {
               "Type":"ANGRY",
               "Confidence":0.012820698320865631
            },
            {
               "Type":"DISGUSTED",
               "Confidence":0.011646679602563381
            }
         ],
         "Pose":{
            "Roll":-27.842042922973633,
            "Yaw":-6.863317012786865,
            "Pitch":6.0284576416015625
         },
         "Quality":{
            "Brightness":93.38902282714844,
            "Sharpness":92.22801208496094
         },
         "Confidence":99.99991607666016
      }
   ],
   "ResponseMetadata":{
      "RequestId":"dd18647f-736c-44a9-aaa3-6c9782c657c7",
      "HTTPStatusCode":200,
      "HTTPHeaders":{
         "x-amzn-requestid":"dd18647f-736c-44a9-aaa3-6c9782c657c7",
         "content-type":"application/x-amz-json-1.1",
         "content-length":"23339",
         "date":"Mon, 20 Mar 2023 20:50:38 GMT"
      },
      "RetryAttempts":0
   }
}

With the above response, we will draw the faces and write the emotions in the image.

image = Image.open(image_name)
image_width, image_height = image.size
draw = ImageDraw.Draw(image)

line_width = 3
for item in rekognition_response.get('FaceDetails'):
    bounding_box = item['BoundingBox']
    width = image_width * bounding_box['Width']
    height = image_height * bounding_box['Height']
    left = image_width * bounding_box['Left']
    top = image_height * bounding_box['Top']

    left = int(left)
    top = int(top)
    width = int(width) + left
    height = int(height) + top

    draw.rectangle(((left, top), (width, height)),
                    outline='red', width=line_width)

    face_emotion_confidence = 0
    face_emotion = None
    for emotion in item.get('Emotions'):
        if emotion.get('Confidence') >= face_emotion_confidence:
            face_emotion_confidence = emotion['Confidence']
            face_emotion = emotion.get('Type')

    draw.text((left, top), face_emotion, 'white', font=font)

This is the result calling the image variable.

This is the complete code.

import boto3
from PIL import Image, ImageDraw, ImageFont

rekognition = boto3.client('rekognition', aws_access_key_id='<YOUR_ACCESS_KEY>',
                           aws_secret_access_key='<YOUR_SECRET_KEY>',
                           region_name='us-east-1')

font = ImageFont.truetype('arial.ttf', 30)

def detect_faces_and_emotions(image_name: str):
    with open(image_name, 'rb') as image_data:
        response_content = image_data.read()

    rekognition_response = rekognition.detect_faces(
        Image={'Bytes': response_content}, Attributes=['ALL'])

    print(rekognition_response)

    image = Image.open(image_name)
    image_width, image_height = image.size
    draw = ImageDraw.Draw(image)

    line_width = 3
    for item in rekognition_response.get('FaceDetails'):
        bounding_box = item['BoundingBox']
        width = image_width * bounding_box['Width']
        height = image_height * bounding_box['Height']
        left = image_width * bounding_box['Left']
        top = image_height * bounding_box['Top']

        left = int(left)
        top = int(top)
        width = int(width) + left
        height = int(height) + top

        draw.rectangle(((left, top), (width, height)),
                    outline='red', width=line_width)

        face_emotion_confidence = 0
        face_emotion = None
        for emotion in item.get('Emotions'):
            if emotion.get('Confidence') >= face_emotion_confidence:
                face_emotion_confidence = emotion['Confidence']
                face_emotion = emotion.get('Type')

        draw.text((left, top), face_emotion, 'white', font=font)

    return image

result_1 = detect_faces_and_emotions('people.jpg')
result_1

In this repository, you can find the code, font file, and images.

And that's it! With just a few lines of Python code and the power of AWS Rekognition, you can create a facial recognition app capable of detecting faces with their emotions in an image.

Thanks for reading

Thank you very much for reading. I hope you found this article interesting and may be useful in the future. If you have any questions or ideas you need to discuss, it will be a pleasure to collaborate and exchange knowledge.