To validate the authenticity of synthetically generated images, we've integrated three sophisticated techniques.
The script iterates through each fingerprint image, utilizing the Scale-Invariant Feature Transform (SIFT) algorithm for keypoint detection and descriptor computation. A FLANN-based matcher is employed for keypoint matching, with good matches filtered using Lowe's ratio test. Subsequently, a matching score is calculated as the ratio of good matches to total keypoints, enabling the identification of fingerprint similarities. This code serves as a valuable tool for automating the comparison and analysis of fingerprint images for the generated images.
This function is aimed at detecting fingerprints within an image using a line detection approach through a series of processing steps. Firstly, the image is converted to grayscale to simplify subsequent operations. Then, a Gaussian blur is applied to reduce noise. Following this, the Canny edge detection algorithm is employed to identify edges within the image. Finally, the Hough line transformation is utilized to detect lines within the edge-detected image. If lines are detected, indicating potential fingerprint ridge patterns, the image is a fingerprint.
This function is designed to detect fingerprints within an image by identifying contours. Firstly, the image is converted to grayscale to simplify subsequent operations. Next, a Gaussian blur is applied to reduce noise. Subsequently, the Sobel operator is utilized to compute the gradient magnitude, which highlights edges within the image. The magnitude image is then binarized using Otsu's thresholding method to create a binary image. Morphological closing is performed to further enhance the binary image. Finally, contours are detected and if contours are found, indicating potential fingerprint ridge patterns.