compare_faces ¶

def compare_faces(
    self,
    source_image: "capo_rekognition.types.image.Image",
    target_image: "capo_rekognition.types.image.Image",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    similarity_threshold: Optional["capo_rekognition.types.percent.Percent"] = None,
    quality_filter: Optional[
        "capo_rekognition.types.quality_filter.QualityFilter"
    ] = None,
) -> "capo_rekognition.types.compare_faces_response.CompareFacesResponse":
    """<p>Compares a face in the <i>source</i> input image with each of the 100 largest faces detected in the <i>target</i> input image. </p> <p> If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image. </p> <note> <p>CompareFaces uses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that you compare the target image against multiple source images. If you plan to use <code>CompareFaces</code> to make a decision that impacts an individual's rights, privacy, or access to services, we recommend that you pass the result to a human for review and further validation before taking action.</p> </note> <p>You pass the input and target images either as base64-encoded image bytes or as references to images in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file. </p> <p>In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match. </p> <note> <p>By default, only faces with a similarity score of greater than or equal to 80% are returned in the response. You can change this value by specifying the <code>SimilarityThreshold</code> parameter.</p> </note> <p> <code>CompareFaces</code> also returns an array of faces that don't match the source image. For each face, it returns a bounding box, confidence value, landmarks, pose details, and quality. The response also returns information about the face in the source image, including the bounding box of the face and confidence value.</p> <p>The <code>QualityFilter</code> input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. Use <code>QualityFilter</code> to set the quality bar by specifying <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>. If you do not want to filter detected faces, specify <code>NONE</code>. The default value is <code>NONE</code>. </p> <p>If the image doesn't contain Exif metadata, <code>CompareFaces</code> returns orientation information for the source and target images. Use these values to display the images with the correct image orientation.</p> <p>If no faces are detected in the source or target images, <code>CompareFaces</code> returns an <code>InvalidParameterException</code> error. </p> <note> <p> This is a stateless API operation. That is, data returned by this operation doesn't persist.</p> </note> <p>For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.</p> <p>This operation requires permissions to perform the <code>rekognition:CompareFaces</code> action.</p>

    Args:
        source_image: <p>The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>
        target_image: <p>The target image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes is not supported. </p> <p>If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the <code>Bytes</code> field. For more information, see Images in the Amazon Rekognition developer guide.</p>
        similarity_threshold: <p>The minimum level of confidence in the face matches that a match must meet to be included in the <code>FaceMatches</code> array.</p>
        quality_filter: <p>A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't compared. If you specify <code>AUTO</code>, Amazon Rekognition chooses the quality bar. If you specify <code>LOW</code>, <code>MEDIUM</code>, or <code>HIGH</code>, filtering removes all faces that don’t meet the chosen quality bar. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify <code>NONE</code>, no filtering is performed. The default value is <code>NONE</code>. </p> <p>To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.</p>

    Raises:
        capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
        capo_rekognition.errors.image_too_large_exception.ImageTooLargeException: <p>The input image size exceeds the allowed limit. If you are calling DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. </p>
        capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</p>
        capo_rekognition.errors.invalid_image_format_exception.InvalidImageFormatException: <p>The provided image format is not supported. </p>
        capo_rekognition.errors.invalid_parameter_exception.InvalidParameterException: <p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>
        capo_rekognition.errors.invalid_s3_object_exception.InvalidS3ObjectException: <p>Amazon Rekognition is unable to access the S3 object specified in the request.</p>
        capo_rekognition.errors.provisioned_throughput_exceeded_exception.ProvisionedThroughputExceededException: <p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>
        capo_rekognition.errors.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
        capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To compare two images
        This operation compares the largest face detected in the source image with each face detected in the target image.

        >>> client.compare_faces(source_image={'S3Object': {'Bucket': 'mybucket', 'Name': 'mysourceimage'}}, target_image={'S3Object': {'Bucket': 'mybucket', 'Name': 'mytargetimage'}}, similarity_threshold=90)
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.compare_faces_request.CompareFacesRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.compare_faces_response.CompareFacesResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.compare_faces

        output, http_response = (
            capo_rekognition._operations.rekognition_service.compare_faces.compare_faces(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CompareFaces")
    )
    input_: capo_rekognition.types.compare_faces_request.CompareFacesRequest = {
        "source_image": source_image,
        "target_image": target_image,
    }
    if similarity_threshold is not None:
        input_["similarity_threshold"] = similarity_threshold
    if quality_filter is not None:
        input_["quality_filter"] = quality_filter

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output