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 12 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

show abstract

“…Here, the most promising settings are to keep the auto-focus activated and if available use the macro mode. Additionally, the flash should be enabled [29,37]. External extensions like additional lights and macro lenses are considered as beneficial by Sagiroglu et al [38].…”

Section: General Purpose Devicesmentioning

confidence: 99%

“…The result is a binary image with a separation between finger image area and background. The above approach is widely adopted, modified to meet different prerequisites, and further investigated by many authors [37,46,54,55]. Ravi and Sivanath [27] showed that extending the Cr component with information of the HSV and nRGB color space enables a precise isolation of a finger.…”

Section: Finger Detection and Segmentationmentioning

confidence: 99%

“…Alternatively, other color models such as CMYK (magenta channel) [9] and CIELAB [39] were also investigated. This approach was adopted in many other preprocessing workflows similar to [37,46,55]. Because of prerequisites during the capturing process, most approaches considered only the largest segmented area as fingerprint [37,55].…”

Section: Finger Detection and Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Touchless 2D Fingerprint Recognition

Rathgeb,

Priesnitz

2020

Preprint

View full text Add to dashboard Cite

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios. This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work.

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 12 publications

References 15 publications

التحقق من البشر بواسطة تبني شبكة تعلم عميق لصور بصمات الاصابع

التحقق من البشر بواسطة تبني شبكة تعلم عميق لصور بصمات الاصابع

An Overview of Touchless 2D Fingerprint Recognition

Towards smartphone-based touchless fingerprint recognition

Contact Info

Product

Resources

About