LAUSR

The study introduces a new threshold method based on a neutrosophic set. The proposal applies the neutrosophic overset and underset concepts for thresholding the image. The global threshold method and the adaptive threshold method were used as the two types of thresholding methods in this article. Images could be symmetrical or asymmetrical in professional disciplines; the government maintains facial image databases as symmetrical. General-purpose images do not need to be symmetrical. Therefore, it is essential to know how thresholding functions in both scenarios. Since the article focuses on biometric image data, face and fingerprint data were considered for the analysis. The proposal provides six techniques for the global threshold method based on neutrosophic membership, indicating neutrosophic TF overset (NOTF), neutrosophic TI overset (NOTI), neutrosophic TIF overset (NOTIF), neutrosophic TF underset (NUTF), neutrosophic TI underset (NUTI), neutrosophic TIF underset (NUTIF); similarly, in this study, the researchers generated six novel approaches for the adaptive method. These techniques involved an investigation using biometric data, such as fingerprints and facial images. The achievement was 98% accurate for facial image data and 100% accurate for fingerprint data.