Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Singh, Law Kumar; Khanna, Munish; Garg, Hitendra

doi:10.4018/ijismd.2020010103

Cited by 16 publications

(3 citation statements)

References 23 publications

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“…These methods can also be employed on parameter optimization for the prediction of a number of practical problems like software testing, software fault prediction, Glaucoma, Covid-19, glucose, mammography, cardiac problems, and plant leaf disease etc. (Khanna, Chauhan & Sharma, 2019;Singh, Khanna & Garg, 2020;Thawkar, Singh & Khanna, 2021;Singh, Garg & Khanna, 2021)…”

Section: Performance Analysis Of Ascbbo and Scbbomentioning

confidence: 99%

Alternated Superior Chaotic Biogeography-Based Algorithm for Optimization Problems

Kumar

Rani

2022

International Journal of Applied Metaheuristic Computing

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In this study, we consider a switching strategy that yields a stable desirable dynamic behaviour when it is applied alternatively between two undesirable dynamical systems. From the last few years, dynamical systems employed “chaos1 + chaos2 = order” and “order1 + order2 = chaos” (vice-versa) to control and anti control of chaotic situations. To find parameter values for these kind of alternating situations, comparison is being made between bifurcation diagrams of a map and its alternate version, which, on their own, means independent of one another, yield chaotic orbits. However, the parameter values yield a stable periodic orbit, when alternating strategy is employed upon them. It is interesting to note that we look for stabilization of chaotic trajectories in nonlinear dynamics, with the assumption that such chaotic behaviour is not desirable for a particular situation. The method described in this paper is based on the Parrondo’s paradox, where two losing games can be alternated, yielding a winning game, in a superior orbit.

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Section: Performance Analysis Of Ascbbo and Scbbomentioning

confidence: 99%

Alternated Superior Chaotic Biogeography-Based Algorithm for Optimization Problems

Kumar

Rani

2022

International Journal of Applied Metaheuristic Computing

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“…The accuracy is 96.7% and sensitivity is 95.1%. Intensity-based feature extraction has been used to extract multi-features [13], and a deep learning-based model was adopted to classify the images using standard datasets [14,15]. OC-based segmentation is a challenging task to detect glaucoma.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

et al. 2021

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Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing <=99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

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“…(Singh,L.K.etal. ;2020) Mostofthepractitionershavepresentedtheirwork,whichislimitedtooneortwonormalization techniques;however,inthisempiricalstudy,wehaveappliedthreenormalizationtechniques(Min-Max,Z-Score,andMedian-MAD).Similarly,theFusiontechniquesimplementedinthestudyare WeightedsumandWeightedproduct,whichismissingintheexistingliterature.…”

Section: Literature Reviewmentioning

confidence: 99%

Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System

Singh

Khanna

Thawkar

et al. 2021

International Journal of Information System Modeling and Design

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Biometrics is the science that deals with personal human physiological and behavioral characteristics such as fingerprints, handprints, iris, voice, face recognition, signature recognition, ear recognition, and gait recognition. Recognition using a single trait has several problems and multimodal biometrics system is one of the solutions. In this work, the novel and imperative biometric feature gait is fused with face and ear biometric features for authentication and to overcome problems of the unimodal biometric recognition system. The authors have also applied various normalization methods to sort out the best solution for such a challenge. The feature fusion of the proposed multimodal biometric system has been tested using Min-Max and Z-score techniques. The computed results demonstrate that Z-Score outperforms the Min-Max technique. It is deduced that the Z-score is a promising method that generates a high recognition rate of 95% and a false acceptance rate of 10%.

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Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Cited by 16 publications

References 23 publications

Alternated Superior Chaotic Biogeography-Based Algorithm for Optimization Problems

Alternated Superior Chaotic Biogeography-Based Algorithm for Optimization Problems

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System

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