Author(s) :
C. Anitha Mary
Article Name :
Revolutionizing Palm Print Recognition with Advanced Sift-Based Feature Analysis Techniques
Abstract :
Palm print recognition has emerged as a robust biometric authentication method due to its distinctiveness and reliability. This paper explores the revolution in palm print recognition driven by advanced feature analysis techniques, focusing on the integration of sophisticated preprocessing and feature extraction and classification methods. Effective palm print recognition relies heavily on preprocessing technique such as noise reduction, contrast enhancement, and image filtering. Thus, this study emphasize the role of Gabor filter excel in image processing by effectively capturing spatial frequency and orientation information. The paper highlights the use of Local Ternary Patterns (LTrP) and Local Modified Ternary Patterns (LMTrP) to enhance the accuracy and robustness of feature extraction in palm print matching and identification. Additionally, the Scale Invariant Feature Transform (SIFT) used to further strengthens the system by providing further enhancing the reliability and rotation. Moreover, this paper implemented by using python programming language. Overall, this work provides insights into their combined impact on the accuracy and reliability of palm print recognition systems, marking a significant advancement in biometric authentication technology.
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