Random Projection-Based Cancelable Iris Biometrics for Human Identification Using Deep Learning

被引:0
作者
Rani, Rajneesh [1 ]
Dhir, Renu [1 ]
Sonkar, Kirti [1 ]
机构
[1] NIT, Dept Comp Sci & Engn, Jalandhar 144011, Punjab, India
关键词
Cancelable biometrics; Template protection; Deep learning; Random projection; CNN; GRU; HYBRID APPROACH; SECURE; FACE;
D O I
10.1007/s13369-023-08190-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cancelable biometrics serves as an effective countermeasure against various template attacks launched by intruders, safeguarding the biometric system. This paper proposes a cancelable approach with a novel feature extraction technique for iris recognition, known as the hybrid architecture of the convolutional neural network (CNN) and GRU (gated recurrent unit). To provide cancelability to the system, the paper makes use of a random projection technique. The proposed method has the best outcome in terms of accurate identification. The method is validated on two Iris datasets IITD and MMU, which show promising results on the equal error rate (EER) and accuracy. The proposed model provides 0.02 and 0.045 EER for IITD and MMU, respectively, and accuracy 0.98 and 0.933%, for IITD and MMU Iris dataset, respectively, which is very high compared to other methodologies. The proposed hybrid architecture is being used for a cancelable biometric system for the first time based on literature review. The efficiency of the proposed method is high when validated on the datasets.
引用
收藏
页码:3815 / 3828
页数:14
相关论文
共 50 条
[21]   Cancelable templates for secure face verification based on deep learning and random projections [J].
Ali, Arslan ;
Migliorati, Andrea ;
Bianchi, Tiziano ;
Magli, Enrico .
EURASIP JOURNAL ON INFORMATION SECURITY, 2024, 2024 (01)
[22]   Random Projection-Based Feature Transformation Using Metaheuristic Optimization Algorithm [J].
Eslam Hamouda ;
A. S. Abohamama ;
Mayada Tarek .
Arabian Journal for Science and Engineering, 2021, 46 :8345-8353
[23]   A deep learning approach for person identification using ear biometrics [J].
Ramar Ahila Priyadharshini ;
Selvaraj Arivazhagan ;
Madakannu Arun .
Applied Intelligence, 2021, 51 :2161-2172
[24]   Projection-based deep learning super-resolution for CBCT dose reduction [J].
Thummerer, Adrian ;
Hofmaier, Jan ;
Belka, Claus ;
Landry, Guillaume ;
Kurz, Christopher .
RADIOTHERAPY AND ONCOLOGY, 2024, 194 :S3930-S3934
[25]   A Deep Learning Based Approach to Iris Sensor Identification [J].
Zabin, Ananya ;
Bourlai, Thirimachos .
2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 2020, :827-834
[26]   Multi-instance cancelable iris authentication system using triplet loss for deep learning models [J].
Mulagala Sandhya ;
Mahesh Kumar Morampudi ;
Indragante Pruthweraaj ;
Pranay Sai Garepally .
The Visual Computer, 2023, 39 :1571-1581
[27]   TOWARDS CANCELABLE MULTI-BIOMETRICS BASED ON BLOOM FILTERS: A CASE STUDY ON FEATURE LEVEL FUSION OF FACE AND IRIS [J].
Rathgeb, Christian ;
Gomez-Barrero, Marta ;
Busch, Christoph ;
Galbally, Javier ;
Fierrez, Julian .
2015 INTERNATIONAL WORKSHOP ON BIOMETRICS AND FORENSICS (IWBF), 2015,
[28]   Multi-instance cancelable iris authentication system using triplet loss for deep learning models [J].
Sandhya, Mulagala ;
Morampudi, Mahesh Kumar ;
Pruthweraaj, Indragante ;
Garepally, Pranay Sai .
VISUAL COMPUTER, 2023, 39 (04) :1571-1581
[29]   Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly [J].
Park, Yoon Jung ;
Ro, Hyocheol ;
Lee, Nam Kyu ;
Han, Tack-Don .
APPLIED SCIENCES-BASEL, 2019, 9 (18)
[30]   Semantic segmentation of 3D LiDAR data using deep learning: a review of projection-based methods [J].
Alok Jhaldiyal ;
Navendu Chaudhary .
Applied Intelligence, 2023, 53 :6844-6855