Touch-less fingerphoto feature extraction, analysis and matching using monogenic wavelets

Birajadar, Parmeshwar; Gupta, Shubham; Shirvalkar, Pushkaraj; Patidar, Varun; Sharma, Utkarsh; Naik, Ameya K.; Gadre, Vikram M.

doi:10.1109/iconsip.2016.7857436

Cited by 11 publications

(2 citation statements)

References 15 publications

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“…The dataset was composed of 50 subjects, where each subject was associated with 4 images. Consequently, the total number of images in the dataset was reached to 200 24 .…”

Section: Literature Reviewmentioning

confidence: 99%

التحقق من البشر بواسطة تبني شبكة تعلم عميق لصور بصمات الاصابع

Alabdoo,

Yalçınkaya

2024

Baghdad Sci.J

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يمكن اعتبار صورة الإصبع واحدة من أحدث وأكثر التقنيات البيومترية إثارة للاهتمام. يعني ذلك ببساطة صورة بصمة أصبع يتم الحصول عليها عن طريق هاتف ذكي بطريقة لا تتطلب الاتصال المباشر. يقترح هذا البحث نهجًا جديدًا للتحقق من البشر استنادًا إلى صورة الإصبع الفوتوغرافية. يُطلق عليه اسم شبكة الإصبع الفوتوغرافية العميقة المتكررة. تتألف من طبقة الإدخال، وسلسلة من الطبقات الخفية، وطبقة الإخراج والتغذية العكسية الاساسية. يعتمد هذا البحث على اخذ صور فوتوغرافية لكافة الاصابع الشخصية بشكل متسلسل. و يتمتع النظام بالقدرة على التبديل بين أوزان كل إصبع فوتوغرافي فردي وتوفير التحقق. تم انشاء قاعدة بينات من عدد كبير من صور الأصابع الفوتوغرافية، وتم تنظيمها وتقسيمها واستخدامها كمجموعة بيانات مفيدة في هذا البحث. تم التوصل الى نتائج عالية في الدقة في التحقق الشخصي عن طريق استخدام الصور الفوتوغرافية للاصابع.

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“…The dataset was composed of 50 subjects, where each subject was associated with 4 images. Consequently, the total number of images in the dataset was reached to 200 24 .…”

Section: Literature Reviewmentioning

confidence: 99%

التحقق من البشر بواسطة تبني شبكة تعلم عميق لصور بصمات الاصابع

Alabdoo,

Yalçınkaya

2024

Baghdad Sci.J

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“…In our work [6] reported at an international conference, we have proposed monogenic-wavelet-based phase congruency features for touchless fingerprint enhancement, which are effectively used for extraction of minutiae features. In this work, we have proposed monogenic-waveletbased phase congruency features for touchless fingerprint enhancement, which are effectively used for extraction of minutiae features.…”

Section: Introductionmentioning

confidence: 99%

Towards smartphone-based touchless fingerprint recognition

et al. 2019

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The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.

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