Iris recognition using machine learning from smartphone captured images in visible light

Khan, Fahim Faysal; Akif, Ahnaf; Haque, Masudul

doi:10.1109/ictp.2017.8285897

Cited by 10 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The iris image of size 128 x 128 pixels is considered. This size of the image will be too heavy to train the classifiers, and training will take an extremely long time [25]. One of the solutions to this can be downscaling the iris image size, which may result in the loss of critical information.…”

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

“…One of the solutions to this can be downscaling the iris image size, which may result in the loss of critical information. Wavelet decomposition provides a solution to this challenge since wavelets have localized frequency data, allowing features with similar resolutions to be matched [25]. When a 2-d wavelet transformation is performed to an image, it decomposes it into four segments: LL, LH, HL, and HH [25].…”

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

“…Wavelet decomposition provides a solution to this challenge since wavelets have localized frequency data, allowing features with similar resolutions to be matched [25]. When a 2-d wavelet transformation is performed to an image, it decomposes it into four segments: LL, LH, HL, and HH [25]. Approximation of the image is what the LL stands for; the horizontal detail, vertical detail, and diagonal detail of the image are represented by LH, HL, and HH, respectively.…”

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

“…The generated feature vector was 2D and had 8 x 8 dimensions. Before training the classifier, it was converted to a 1D vector of length 64 by placing the side of the row by the side, as shown in figure 2 [25].…”

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

See 3 more Smart Citations

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Khade

Gite²,

Thepade³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris-PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM+RF+NB, SVM+RF+RT, and RF+SVM+MLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 x 4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements. INDEX TERMS Iris presentation attacks, liveness detection, Haar transformation, DCT, hybrid transform I.

show abstract

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

Section: Proposed Iris Presentation Attacks Detection Using Dct Haar ...mentioning

confidence: 99%

See 2 more Smart Citations

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Khade

Gite²,

Thepade³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…MLE has also been applied to the VLC field. The authors in [97] showed the applicability and feasibility of different MLE techniques based on iris recognition through smart phone captured images. Authors trained different classifiers and used histogram equalization processes to maximize accuracy.…”

Section: Related Work In Denoising Schemes For Vlcmentioning

confidence: 99%

Investigation of vehicle to vehicle communication system using visible light technology

Farahneh¹

2021

Preprint

View full text Add to dashboard Cite

Visible Light Communication (VLC) has emerged as an attractive alternative to radio frequency (RF) communication, due to cost-effectiveness and being license free. It has also proved its applicability in automotive applications, as an alternative and/or a complement to the traditional RF-based communications. We investigate the suitability of VLC in Intelligent Transportation Systems (ITS) and discuss its advantages in terms of safety enhancement and improved efficiency of the ITS. In this thesis, a VLC-based Vehicle-to-Vehicle (V2V) system in practical environments, considering both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths are presented. The thesis investigates robust communication between a Light Emitting Diodes (LEDs) based VLC emitter and Photodiodes (PDs) based VLC receiver. For consideration of a V2V communication system, we consider transmitter on vehicle headlights and receivers on taillight making 2 _ 2 Multiple-Input-Multiple-Output (MIMO) communication link. A closed-form expression of the Channel Impulse Response (CIR) is derived and the effect of various channel parameters is analyzed. Optical-Orthogonal Frequency Division Multiplexing (O-OFDM) with adaptive modulation schemes is proposed for system improvement. Its performance is evaluated in terms of Inter Symbol Interference (ISI) mitigation, and data rate improvement. Moreover, the effect of sunlight on the V2V-VLC Two denoising schemes are proposed and evaluated as a solution to combat the effect of the solar irradiance on the VLC signal. Firstly, we use a differential receiver for denoising purposes followed by k-Nearest Neighbour (kNN) based adaptive _ltering algorithm, which is a supervised Machine Learning (MLE) technique. The shadowing effect is also studied. Moreover, an application of VLC in Foglet based ITS is described. The simulation validation of the VLC-based V2V system is performed under various environmental conditions and scenarios. Obtained results emphasize the suitability of VLC technology for automobile applications,.

show abstract

Multi-instance cancelable iris authentication system using triplet loss for deep learning models

et al. 2022

View full text Add to dashboard Cite

Iris recognition using machine learning from smartphone captured images in visible light

Cited by 10 publications

References 12 publications

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Investigation of vehicle to vehicle communication system using visible light technology

Multi-instance cancelable iris authentication system using triplet loss for deep learning models

Contact Info

Product

Resources

About