Implementation of Convolutional Neural Network to Classification Gender based on Fingerprint

Gustisyaf, Ahmad Ilham; Sinaga, Ardiles

doi:10.5815/ijmecs.2021.04.05

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Several studies have explored the use of machine learning techniques to classify the gender of fingerprints, using various features and algorithms [9][10][11][12]. In one study, Yadav, Jaffery, and Singh [13] developed a multilayer perceptron neural network that used various fingerprint traits, such as white line count, ridge thickness, and ridge-to-valley ratios, to identify gender.…”

Section: A Classification Methodsmentioning

confidence: 99%

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Vidhya Keren T,

Serin J,

Mary Ivy Deepa I S

et al. 2023

JAIT

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Gender classification is used in numerous applications such as biometrics, criminology, surveillance, HCI, and business profiling. Although biometric factors like gait, face, hand shape, and iris have been used to classify people into genders, the majority of research has focused on facial traits due to their more recognisable qualities. This research employs fingerprints to classify gender, with the intention of being relevant for future studies. Several methods for gender classification utilising fingerprints have been presented in the literature, including ANN, KNN, Naive Bayes, the Gaussian mixture model, and deep learning-based classifiers. Although these classifiers have shown good classification accuracy, gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy, computation, and running time. In this paper, a CNN-SVM hybrid framework for gender classification from fingerprints is proposed, where preprocessing, feature extraction, and classification are the three main components. The main goal of this study is to use CNN to extract fingerprint information. These features are then sent to an SVM classifier to determine gender. The hybrid model's performance measures are examined and compared to those of the conventional CNN model. Using a CNN-SVM hybrid model, the accuracy of gender classification based on fingerprints was 99.25%.

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Section: A Classification Methodsmentioning

confidence: 99%

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Vidhya Keren T,

Serin J,

Mary Ivy Deepa I S

et al. 2023

JAIT

View full text Add to dashboard Cite

show abstract

“…Gustisyaf and Sinaga [16], the way toward distinguishing fingerprints is one of the significant, simple to do multifariousness strategies, the cost is cheap, and a dactyloscopy authority does the particular outgrowth. Classified gender by fingerprint using the CNN method, and three models were created to determine sex, with a complete of 49,270 image data that included test data and training data by classifying two classes, male and female.…”

Section: Related Workmentioning

confidence: 99%

Store product classification using convolutional neural network

Wiryana

Harmanto²,

Fauzi³

et al. 2023

IJ-AI

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Stores sells consumer goods, mainly food products and other household products at retail. The products sold in stores vary greatly, in order to be time efficient in the fast-paced era and the current technological era requires artificial intelligence technology. In the artificial intelligence branch, there is a specific or detailed learning process known as deep learning. One of the branches of deep learning is the convolutional neural network(CNN). This research intends to employ a CNN architecture to facilitate and streamline the time and cost of the store’s product sorting process. The test is conducted with 1,050 product images divided into 35 labels and divided into three data, namely 80% data training 10% data validation and 10% data test. The image used is preprocessed with a size of 256×256 pixels. The data was trained with six convolution layers and an epoch of 50 with an execution time of 33 minutes so as to achieve an accuracy of 91.37%.

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“…Artificial intelligence (AI), including machine and deep learning, has been increasingly applied to replace human intelligence and other mathematical algorithms in many fields, such as IT operations and management [19], healthcare [20,21], transportation [22][23][24], education [25], smart cities [26,27], accident and disaster management [28], big data [29], improving computing algorithmic [30,31], spam detection [32], and gender classification [33]. We have mentioned earlier that there are only four ML-based methods for the estimation of contributors in DNA mixtures.…”

Section: Machine Learning In Dna Profilingmentioning

confidence: 99%

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

et al. 2022

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DNA profiling involves the analysis of sequences of an individual or mixed DNA profiles to identify the persons that these profiles belong to. A critically important application of DNA profiling is in forensic science to identify criminals by finding a match between their blood samples and the DNA profile found on the crime scene. Other applications include paternity tests, disaster victim identification, missing person investigations, and mapping genetic diseases. A crucial task in DNA profiling is the determination of the number of contributors in a DNA mixture profile, which is challenging due to issues that include allele dropout, stutter, blobs, and noise in DNA profiles; these issues negatively affect the estimation accuracy and the computational complexity. Machine-learning-based methods have been applied for estimating the number of unknowns; however, there is limited work in this area and many more efforts are required to develop robust models and their training on large and diverse datasets. In this paper, we propose and develop a software tool called TAWSEEM that employs a multilayer perceptron (MLP) neural network deep learning model for estimating the number of unknown contributors in DNA mixture profiles using PROVEDIt, the largest publicly available dataset. We investigate the performance of our developed deep learning model using four performance metrics, namely accuracy, F1-score, recall, and precision. The novelty of our tool is evident in the fact that it provides the highest accuracy (97%) compared to any existing work on the most diverse dataset (in terms of the profiles, loci, multiplexes, etc.). We also provide a detailed background on the DNA profiling and literature review, and a detailed account of the deep learning tool development and the performance investigation of the deep learning method.

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Implementation of Convolutional Neural Network to Classification Gender based on Fingerprint

Cited by 11 publications

References 14 publications

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Store product classification using convolutional neural network

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

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