A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA

Sasirekha, K.; Thangavel, K.

doi:10.1504/ijbm.2018.090133

Cited by 3 publications

(2 citation statements)

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“…Several classifications of fingerprint exist in the research field. As mentioned in [2], among the most used ones, we found the three main categories (Arch, loop and whorl) as shown in figure 1. Other sub-categories was listed by author in [3] deriving from the Henry classification which provides five classes of fingerprints .…”

Section: Basics On Biometric Fingerprintmentioning

confidence: 83%

A Novel Approach for an Interoperable Biometric Verification

Beqqal¹,

Azizi²,

Lanet

2018

Int. J. Interact. Mob. Technol.

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<span style="font-family: 'Times New Roman','serif'; font-size: 9pt; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Increasingly, safety is present in everyday life, at work, at home, in recreational environments, and in all places where there is a flow of people who share a common space. Using biometric fingerprint technique for identity verification has become a primordial due to the Reduced costs of implementation, maturity of this technology in the market and the ease of use. Many implementations exist using this technique , however, these systems are hard coupled with the environment in which they were developed and the hardware used to capture biometric data. Consequently, the interoperability issue is hard present in case of changing the environment or using new fingerprint reader. In this paper, we propose a new architecture to solve this problem by dividing the main biometric application to layers based on a flexible communication between components and supporting heterogeneous platforms and biometric readers.</span>

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Section: Basics On Biometric Fingerprintmentioning

confidence: 83%

A Novel Approach for an Interoperable Biometric Verification

Beqqal¹,

Azizi²,

Lanet

2018

Int. J. Interact. Mob. Technol.

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“…In the domain of artificial intelligence, neural networks are promising avenue of research to obtain the complex representations between input and target output [33][34][35][36][37].…”

Section: Bpn For Classificationmentioning

confidence: 99%

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

et al. 2021

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Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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An empirical study of dermatoglyphics fingerprint pattern classification for human behavior analysis

Bhimrao

Gupta

2023

Soc. Netw. Anal. Min.

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A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA

Cited by 3 publications

References 0 publications

A Novel Approach for an Interoperable Biometric Verification

A Novel Approach for an Interoperable Biometric Verification

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

An empirical study of dermatoglyphics fingerprint pattern classification for human behavior analysis

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