Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Patil, Awanikumar P.; Kruthi, R; Gornale, Shivanand S.

doi:10.5815/ijigsp.2019.05.04

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…The purpose of LPQ operator is the identification of texture by figuring it locally at each pixel area and exhibiting the subsequent codes as a histogram. LPQ is, moreover, a generalization of LBP and BSIF [18]. Additionally, LPQ can discriminate phase information from an image without losing the data at high frequencies.…”

Section: Local Phase Quantization (Lpq)mentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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In this paper, the Osteoarthritis (OA) analysis in knee radiographic images using artificial neural networks (ANN) is considered. In Osteoarthritis, mobility is restricted and bones rub each other causing extreme pain in knee due to cartilage disintegration. The cartilage destruction is minimal in the initial stage of OA. It is observed that a small number of researchers have implemented identification and grading of Osteoarthritis utilizing their own datasets for experimentation. However, there is still need of automatic computer aided techniques to detect Osteoarthritis for early recognition. In this work, a dataset of 1650 radiographic images of knee joints of OA patients are collected from different hospitals and have been annotated by two different orthopedic surgeons as per the Kellgren and Lawrence (KL) grading system. To automate this grading procedure, the local phase quantization and multi-block projection profile features are computed from the images and then presented to artificial neural network to classify the images based on the KL grading of the severity of the disease. The classification accuracy of 98.7% and 98.2% with reference to surgeon-1 and surgeon-2 opinions, respectively, is achieved.

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Section: Local Phase Quantization (Lpq)mentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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“…Therefore from above mentioned things in 12,13 attains very low Recognition accuracy, 14 offer lack of continuous monitoring, 15 suggest techniques have poor performances in privacy, [16][17][18] imply Biometric data enrollment deprivation, 19,20 lack of Resistance on spoof attacks, hence there is great need to develop an effectual strategy in an emerge field of biometric system.…”

Section: Literature Surveymentioning

confidence: 99%

An ArmurMimus multimodal biometric system for Khosher authentication

Bokade¹,

Kanphade²

2022

Concurrency and Computation

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One of the basic requirements of our modern day society is personal authentication. Biometric recognition should make a human‐like identity determination by identifying its physiological and/or behavioral characteristics. In comparison to traditional knowledge‐based approaches, biometric identification systems have the potential to bring benefits. However, because of the difficulties in extracting non‐class discriminative features, the lack of protection during storage of extracted features, and poor recognition accuracy, most frequently used biometric systems lack model protection and robustness. This research proposed a Mimus multimodal biometric system focused on the combination of multiple modalities and optimal level fusion of features to resolve these problems. Initially, the novel Blob‐funk method extracts the complementary non‐class discriminatory information among different modalities, which accomplishes the biometric data enrollment. Thus, it extracts the different properties by comparing surrounding regions based on finding the local maxima and minima of the function. After extracting the features, they need to be stored in a secure manner in a database. Therefore, the paper incorporates the new code block protection strategy to achieve an effectual protection of continuous monitoring via the generation of non‐invertible features, which is used to create the templates, thus storing them in a database. Finally, the novel Lucynomial logistic regression system incorporates user authentication and thus achieves greater recognition accuracy through estimation of threshold value with confrontation of spoof attacks. Hence, compared to the existing techniques such as SVM, PCA, and DBN, the outcome of the proposed work attains 97.53% accuracy, 0.020% FAR, 96.44% recall, and 97.85% precision, thus exemplifying the competence of the novel system.

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“…KNN will classify by suitable K value which in turn finds the nearest neighbor and provides a class label to un-labeled images [7]. Depending on the types of problem, a variety of different distance measures can be implemented [23] [24]. In this work, City-block distance, Cosine, Correlation and Euclidean distance is considered with K=3 which is empirically fixed throughout the experiment.…”

Section: K Nearest Neighbors (K-nn)mentioning

confidence: 99%

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

Gornale¹,

Patravali²

2019

IJIGSP

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Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

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Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Cited by 15 publications

References 33 publications

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

An ArmurMimus multimodal biometric system for Khosher authentication

Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

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