Latent Lip Print Identification Using Fast Normalized Cross-Correlation

Smacki, Lukasz

doi:10.1109/icbake.2013.37

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Based on 200 lip prints, with an accuracy of 89.5%. Smacki [32] proposed an algorithm to recognize a person using Fast Normalized Cross-Correlation; the background was first removed. Afterward, the upper and lower lips were separated.…”

Section: F Other Methodsmentioning

confidence: 99%

A Comparison of Methods for Extracting Lip Print Features

عنائي,

Mersal,

مصطفى

2023

النشرة المعلوماتية في الحاسبات والمعلومات

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Lip prints and other biometric methods are increasingly used to identify people at crime scenes. Since the early 2000s, researchers have shown increased interest in using these methods. A lip print is biometric, like fingerprints, iris, or another feature in the body. Lip prints are unique to everyone, so they do not change over time. The images are classified through various methods to extract the features from the image based on color, shape, and texture features. A texture feature was the most critical extraction feature. This study examines the challenges and accuracy of previous lip print biometric methods and the various available techniques. In this survey, most researchers used feature extraction based on texture techniques like Hough Transform (HT), Dynamic Time Wrapping (DTW), statistical analysis, edge detection operators, and Local Binary Patterns (LBP). Our literature review covered the past ten years. Hough Transform was the most accurate at 98.49%, followed by Local Binary Patterns and Triangle feature set-based fuzzy at 97%.

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Section: F Other Methodsmentioning

confidence: 99%