An efficient clustering-based segmentation approach for biometric image

Background: Image analysis plays a vital role in the biometric identification system. To achieve the effective outcome of any biometric identification system, the inputted biometric image taken should be of fine quality as it greatly impacts the decision. Image segmentation is a significant aspect of image analysis that must be carried out for enhancing the quality of an image. It efficiently differentiates the foreground and background region of the inputted biometric image and facilitates further image processing simply by providing a segmented binary image which is more coherent to the system. Objective: We present an efficient clustering-based image segmentation approach to obtain the quality segmented binary image that was further processed to get the quality decision in the biometric-based identification system. Methods: A centre of mass-based centroid clustering approach for image segmentation was proposed to perform binarization of an image so as the adequate and operative results can be found. Results: The performance of the proposed approach was applied to different sets of biometric data set having a different number of hand images. This approach provides sharp and lucid images so that good and effective intended results can be obtained. Conclusion: The centroid based clustering approach for image segmentation outperforms the existing clustering approach. In order to measure the quality of the segmented binary image, different statistical performance parameters are used: PSNR, Dunn Index, Silhouette, and Run Time (sec). © 2021 Bentham Science Publishers.