Detection of Ridge Discontinuities in Fingerprint Recognition Influenced by Skin Diseases
نویسندگان
چکیده
منابع مشابه
Fingerprint Recognition Influenced by Skin Diseases
This article is devoted to different skin diseases on fingertips which have an impact to the process of fingerprint acquirement or recognition. There are many people, who suffer under such dermatologic diseases and are therefore excluded from the set of users of a biometric system. The classification of skin diseases is made from the medical point of view. This classification is followed by the...
متن کاملInfluence of Skin Diseases on Fingerprint Recognition
There are many people who suffer from some of the skin diseases. These diseases have a strong influence on the process of fingerprint recognition. People with fingerprint diseases are unable to use fingerprint scanners, which is discriminating for them, since they are not allowed to use their fingerprints for the authentication purposes. First in this paper the various diseases, which might inf...
متن کاملClassification of Skin Diseases and Their Impact on Fingerprint Recognition
This article describes different skin diseases which could have the influence to the process of fingerprint acquirement. There are many people, who suffer under such diseases and are therefore excluded from the set of users of a biometric system and could not e.g. get a visa to the USA or use an access biometric system installed in a company, where they work.
متن کاملInfluence of Skin Diseases on Fingerprint Quality and Recognition
Fingerprint recognition belongs to one of the most often used biometric technologies world‐ wide. It is believed that fingerprints could be used for the recognition of a person in nearly any case; however there exist many cases, where the fingerprint recognition could not be used. There exist some influencing factors [1] that have an impact to the process of finger‐ print recognition, e.g. the ...
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a problem of computer vision applications is to detect regions of interest under dif- ferent imaging conditions. the state-of-the-art maximally stable extremal regions (mser) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: 1) making a component tree of extremal regions’ evolution (enumeration), 2) obtaining region ...
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ژورنال
عنوان ژورنال: International Journal of Advanced Science and Technology
سال: 2018
ISSN: 2005-4238,2005-4238
DOI: 10.14257/ijast.2018.116.02