The quality of fingerprint image greatly affects the performance of minutiae extraction and the process of matching in fingerprint identification system. In order to improve the performance of the fingerprint identification system, a fuzzy techniques used for bothfingerprint image quality analysis and enhancement. First, the quality analysis performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). Afuzzy logic technique that uses Mamdani fuzzy rule model was designed. The fuzzy inference system was able toanalyze and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and fuzzy inference rules. The experimental result obtained using the fuzzy technique were successful, and was able to determine the fingerprint image quality (Oily, Neutral, or Dry) according to their input features.
Eltayeb, A (2021). Fingerprint Image Quality Analysis And Enhancement Using Fuzzy Logic Technique. Afribary. Retrieved from https://tracking.afribary.com/works/fingerprint-image-quality-analysis-and-enhancement-using-fuzzy-logic-technique
Eltayeb, Abdelwahed "Fingerprint Image Quality Analysis And Enhancement Using Fuzzy Logic Technique" Afribary. Afribary, 12 May. 2021, https://tracking.afribary.com/works/fingerprint-image-quality-analysis-and-enhancement-using-fuzzy-logic-technique. Accessed 14 Nov. 2024.
Eltayeb, Abdelwahed . "Fingerprint Image Quality Analysis And Enhancement Using Fuzzy Logic Technique". Afribary, Afribary, 12 May. 2021. Web. 14 Nov. 2024. < https://tracking.afribary.com/works/fingerprint-image-quality-analysis-and-enhancement-using-fuzzy-logic-technique >.
Eltayeb, Abdelwahed . "Fingerprint Image Quality Analysis And Enhancement Using Fuzzy Logic Technique" Afribary (2021). Accessed November 14, 2024. https://tracking.afribary.com/works/fingerprint-image-quality-analysis-and-enhancement-using-fuzzy-logic-technique