Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features

Abdullah, Siti Fairuz; Rahman, Ahmad Fadzli Nizam Abdul; Abas, Zuraida Abal; Saad, Wira Hidayat Mohd

doi:10.17485/ijst/2016/v9i9/84889

Cited by 21 publications

(18 citation statements)

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“…It is worth mentioning that accelerometer sensor features have dominated in this area. Nevertheless, a fingerprint sensor utilized in a smartphone has also been utilized for gender and age classification [87][88][89]. Table 3 summarizes some of the interesting and early works conducted in this field.…”

Section: Personal Traitsmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

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Section: Personal Traitsmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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“…Authors used discrete wavelet transform and singular value decomposition based features with K-Nearest Neighbor Classifier (KNN). In [4], authors extracted global features such as ridge density, ridge thickness to valley ratio(RTVTR) and white lines count, later these global descriptors are fed into multilayer perceptron neural network to classify male and female fingerprints. Use of discrete wavelet transform and principal component analysis is explored in [5], for identification of gender using fingerprints.…”

Section: Related Workmentioning

confidence: 99%

“…Gender identification of person can be done efficiently based on the fingerprints, thanks to ridges of fingerprints which serves as the main feature [4]. It is noted that average ridge density is slightly higher in females than males.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint based Automatic Human Gender Identification

Prabha¹,

Sheetlani²,

Pardeshi³

2017

IJCA

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Human beings have unique and distinct characteristics which are helpful to distinguish one human being from another and thus acts as form of identification. Biometric allows us to identify individuals based on some anatomical structures of body such as fingerprints, face , hand-geometry ear and iris etc. Addition to this soft biometric traits such as gender, age and eye color, voice, accent etc. soft biometric traits help to support traditional biometrics by adding some extra meaningful information. In this context, gender identification becomes a significant task to improve the biometric systems [2]. Gender identification plays a vital role in many applications like human computer interaction, content based indexing, decision making, searching, surveillance and demographic studies. In this paper, we present multi-resolution features based method for gender identification using fingerprints. Our method involves three main steps preprocessing, feature extraction and classification. To do preprocessing we employed contrast limited adaptive histogram equalization, discrete wavelet transform for multi-resolution based feature extraction and classification using feed forward back propagation neural network. In our experiments, we have achieved progressive results on dataset of 750 fingerprints.

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“…It combines compression (dimensionality reduction), feature extraction and classification processes in a single architecture. Until now, CNN has been applied to various applications such as face detection [5]- [10], face recognition [11]- [15], gender recognition [16]- [19], object classification and recognition [20]- [22], character recognition [23]- [25], texture recognition [26], finger-vein [27], etc. Despite the listed advantages, CNN has limitations in terms of cost and speed.…”

Section: Introductionmentioning

confidence: 99%

A design of license plate recognition system using convolutional neural network

Marzuki¹,

Syafeeza²,

Wong³

et al. 2019

IJECE

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This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

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Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features

Cited by 21 publications

References 0 publications

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Fingerprint based Automatic Human Gender Identification

A design of license plate recognition system using convolutional neural network

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