Matching entities tutorial

This subpage will walk you through the basics of how to use matchers available in the iris package. From it you will learn how to: - Use the HammingDistanceMatcher matcher to compute distance between two eyes.

1. Use the HammingDistanceMatcher matcher to compute distance between two eyes.

Load all IR images with opencv-python package.

import cv2

subject1_first_image = cv2.imread("./subject1_first_image.png", cv2.IMREAD_GRAYSCALE)
subject1_second_image = cv2.imread("./subject1_second_image.png", cv2.IMREAD_GRAYSCALE)
subject2_image = cv2.imread("./subject2_image.png", cv2.IMREAD_GRAYSCALE)

Create IRISPipeline object and compute IrisTemplates for all images.

import iris

iris_pipeline = iris.IRISPipeline()

output_1 = iris_pipeline(subject1_first_image, eye_side="left")
subject1_first_code = output_1["iris_template"]

output_2 = iris_pipeline(subject1_second_image, eye_side="left")
subject1_second_code = output_2["iris_template"]

output_3 = iris_pipeline(subject2_image, eye_side="left")
subject2_code = output_3["iris_template"]

Create a HammingDistanceMatcher matcher object.

def __init__(
    self,
    rotation_shift: int = 15,
    nm_dist: Optional[confloat(ge=0, le=1, strict=True)] = None,
    weights: Optional[List[np.ndarray]] = None,
) -> None:
    """Assign parameters.

    Args:
        rotation_shift (int): rotations allowed in matching, converted to shifts in columns. Defaults to 15.
        nm_dist (Optional[confloat(ge=0, le = 1, strict=True)]): nonmatch distance used for normalized HD. Optional paremeter for normalized HD. Defaults to None.
        weights (Optional[List[np.ndarray]]): list of weights table. Optional paremeter for weighted HD. Defaults to None.
    """

matcher = iris.HammingDistanceMatcher()

Call run method and provide two IrisTemplates to compute distances.

def run(self, template_probe: IrisTemplate, template_gallery: IrisTemplate) -> float:

same_subjects_distance = matcher.run(subject1_first_code, subject1_second_code)
different_subjects_distance = matcher.run(subject1_first_code, subject2_code)

Thank you for making it to the end of this tutorial!