Towards smartphone-based touchless fingerprint recognition

Birajadar, Parmeshwar; Haria, Meet; Kulkarni, Pranav; Gupta, Shubham; Joshi, Prasad; Singh, Brijesh; Gadre, Vikram M.

doi:10.1007/s12046-019-1138-5

Cited by 25 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have validated our model using the Indian Institute of Technology Bombay Contactless Fingerprint Dataset (IITB-CFD) [7], a standard public dataset [4], [7], [15], [19], [29], and the Indian Army Inkprint Dataset. Fig.…”

Section: A Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

SSNet: Novel Approach for Fingerprint Recognition in Data-Scarce Scenarios

Pateriya,

Ghule,

Jadav

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Datasetsmentioning

confidence: 99%

“…Fig. 1: Sample Images of IIT Bombay Contactless Fingerprint Dataset [7] (Corresponding author: Saket Pateriya. )…”

Section: Introductionmentioning

confidence: 99%

SSNet: Novel Approach for Fingerprint Recognition in Data-Scarce Scenarios

Pateriya,

Ghule,

Jadav

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Touchless technology developments are centered on analyzing the commands issued by human gestures, motion, speech, emotions, retina, holographic imaging, and other means, where each activity may be mapped to a specific set of activities which then execute the required Fig. 1:Smart technologies model for iBMS system action for the user based on pre-programmed algorithms [41][42][43] .…”

Section: A Model Using Next Generation Smart Technologies For Ibms So...mentioning

confidence: 99%

Role of Building Automation Technology in Creating a Smart and Sustainable Built Environment

Singh¹,

Singh²

2023

Evergreen

View full text Add to dashboard Cite

In today's modern society, people are getting more conscious of the built environment's energy efficiency and sustainability. Sustainable architectural design reduces negative environmental effect by improving energy efficiency, health, comfort, safety, livability for inhabitants, and the ecosystem at large. Other considerations include effective maintenance and optimization of the resources. All of this is achieved by incorporating appropriate technology within the building. Recent developments in the field of IoT, cloud computing, machine learning, artificial intelligence, and other smart wireless technologies could support solutions for building automation systems. Therefore, it is utmost important to determine the potential of these cutting-edge and smart wireless technologies for building automation and management. In this research work, the role of building technologies in creating a smart and sustainable built environment is identified and discussed. To emphasize the importance, a survey is being conducted to identify the current demand for building management systems, as well as their awareness and present scenario.

show abstract

“…Attrish A, Bharat N, Anand V, Kanhangad V (15) Deep CNN IITI-CFD Minutiae 2.19 Birajadar P, Haria M, Kulkarni P (16) VeriFinger…”

Section: Features Extractedmentioning

confidence: 99%

A Study on Structural and Textural Feature Extraction for Contactless Fingerprint Classification

Deepika¹,

Shivakumar²

2022

IJST

View full text Add to dashboard Cite

Objectives:The Recent COVID 19 Pandemic has capped the efficacy of the mostly used existing touch-based fingerprint detection. Hence, the main objective of this study is to develop a lightweight, robust and efficient touchless fingerprint identification model. To cope up with touchless environment demands, an emphasis is made on improvement in local conditions, feature modalities as well as learning environment. Methods: Considering these objectives, the focus of this study is on two different methods for classification of contactless fingerprint. In the first method structural features like minutiae details are extracted followed by classification by SSIM. In the second method GLCM textural features were extracted followed by classification using Random Forest algorithm. In the proposed method, performance assessment is done by considering data samples of 1000 random users that are collected from different benchmark databases like Hong Kong Polytechnic University 3D-fngerprint images Database Version 2.0, Touchless Fingerprint Database of IIT Bombay, IIT Kanpur, IIT Jodhpur. Findings: Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and texture damages can majorly limit its efficacy. Novelty: The proposed model mainly focusses on reducing Equal Error Rate and improving the accuracy of contactless fingerprint classification by extracting textural features rather just sticking to conventional structural feature-minutiae. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827 and a reduced EER of about 0.084. The key novelty of this approach was that it doesn't require any surface 3D reconstruction, rather it employed different mathematical approaches to retrieve surface normal and minutiae information.

show abstract

Towards smartphone-based touchless fingerprint recognition

Cited by 25 publications

References 23 publications

SSNet: Novel Approach for Fingerprint Recognition in Data-Scarce Scenarios

SSNet: Novel Approach for Fingerprint Recognition in Data-Scarce Scenarios

Role of Building Automation Technology in Creating a Smart and Sustainable Built Environment

A Study on Structural and Textural Feature Extraction for Contactless Fingerprint Classification

Contact Info

Product

Resources

About