Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning

Arora, Shefali; Bhatia, M. P. S.

doi:10.1007/s13369-019-04190-1

Cited by 25 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DL methods have been widely used in cybersecurity science. For example, the DL has being found to be applied in modeling biometrics in forensic science for criminal investigations [5] and digital security applications for authentication of individuals [6]. The applications of DL in biometrics recognition and authentication has also received attention in recent years, with various schemes developed to improve digital security and safety issues [7].…”

Section: Open Accessmentioning

confidence: 99%

“…The proposed method although performed a bit less than pre-trained ResNet with 98.55% against 99.88% accuracy, the method achieved 100% precision and recall score on real images, making it better for real-time applications. A study by [6], proposed a CNN architecture for the detection of spoofed fingerprints for authentication tasks in financial applications and banking. The input fingerprints images to the proposed method were preprocessed using contrast enhancement using histogram equalization.…”

Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

“…Few studies on GAN demonstrate the efficacy of DL in generating fingerprint images. Different application tasks of the proposed DL-based methods have been identified such as fingerprint classification [15, 67, 79-81, 97, 100, 101, 111, 112], fingerprint liveness detection [65,74,75,77,106,110], fingerprint recognition and authentication [25,26,76], overlapped fingerprint separation [86], double-identity fingerprint detection [87], fingerprint ROI segmentation [88,89], fingerprint alteration detection [94], fingerprint image enhancement [20,96,107,108], latent fingerprint segmentation [83], latent fingerprint recognition [85], latent fingerprint enhancement [84], fingerprint indexing [90,91], fingerprint pore matching [70,99], partial fingerprint matching [92,93], cancelable recognition system [95], fingerprint spoofing detection [6,109], contactless to contact-based and 3D partial fingerprint images matching [104,105], fingerprint minutiae extraction [71,102,103], fingerprint pore extraction [98], fingerprint generation, and presentation attack detection [113,114], fingerprint recovery scheme [115], and fingerprint l...…”

Section: Task For Fingerprint Biometricsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

View full text Add to dashboard Cite

Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

show abstract

Section: Open Accessmentioning

confidence: 99%

Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

Section: Task For Fingerprint Biometricsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

View full text Add to dashboard Cite

show abstract

“…Convolutional Neural Network is a category of neural networks having capability of giving better performance in terms of complexity and memory requirements [60]. CNN extracts features used for prediction and combines the weights of convolution layers with fully connected layers.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

2020

View full text Add to dashboard Cite

Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

show abstract

“…More specifically, in this survey we will tackle in section 2 twin identification, one of the biggest problems with ocular recognition, applying deep learning (Gautam et al, 2019). In section 3, an improved method to prevent fingerprint spoofing is discussed, potentially solving this biometric system's biggest drawback (Arora and Bhatia, 2019). In section 4, a new method for biometric identification is proposed, based on the heartbeat pulse of the user, utilizing the electrocardiogram (ECG) to recognize different users (Kim and Pan, 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Biometrics: A Survey

López

2019

ADCAIJ

View full text Add to dashboard Cite

Deep learning; biometrics; fingerprint; ocular recognition; convolutional neural networks; electrocardiogram Deep learning has been established in the last few years as the gold standard for data processing, achieving peak performance in image, text and audio understanding. At the same time, digital security is of utmost importance in this day and age, where everyone could get into our personal devices like cellphones or laptops, where we store our most valuable information. One of the possible ways to prevent this is via advanced and personalized security: biometrics. In this survey, it is considered how the scientific advances in the field of deep learning are applied to biometrics in order to enhance the protection of our data. Firstly, a study will be conducted on tackling ocular identification of twins using deep learning. Then, an improved method for avoiding fingerprint spoofing will be presented, thus solving this method's biggest issue. Finally, a brand new method for biometric identification is proposed based on the usage of the user's electrocardiogram. On every one of these methods, the results manage to top the standard alternative performances.

show abstract

Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning

Cited by 25 publications

References 41 publications

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

Deep Learning in Biometrics: A Survey

Contact Info

Product

Resources

About