Finger Type Classification with Deep Convolution Neural Networks

Al-Wajih, Yousif Ahmed; Hamanah, Waleed M.; Abido, M. A.; AL-Sunni, Fouad M.; Alwajih, Fakhraddin

doi:10.5220/0011327100003271

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the proposed EfficientNetB0-PCA-RF was 99.91 percent, while that of the standard EfficientNet-B0 network was only 82.76 percent. And in comparison to previously published research using different techniques [8], [9], [10], [11], [12] summarized in Table 1. In [8], [11] the structure was built for CNN from scratch, and [10] used CNN and SVM, whereas [12] used CNN and Naïve Bayes for various fingerprint classifications.…”

Section: Discussionmentioning

confidence: 89%

“…And in comparison to previously published research using different techniques [8], [9], [10], [11], [12] summarized in Table 1. In [8], [11] the structure was built for CNN from scratch, and [10] used CNN and SVM, whereas [12] used CNN and Naïve Bayes for various fingerprint classifications. Figure 6 shows the comparison of the accuracy of our proposed methods with the previous studies.…”

Section: Discussionmentioning

confidence: 89%

“…In 2022 Ibitayo et al [10] designed a method for identifying a person's gender based on their fingerprints, with an accuracy of 96% using CNN and 94% using SVM (for gender classification). In 2022 Al-Wajih et al [11] using the SOCOFing dataset, the proposed CNN model achieved an accuracy of 89% when classifying fingerprint classes (index, middle, ring, and little fingers). In 2022 Sravanthi et al [12] fingerprint verification using a Support Vector Machine was shown to be effective, and the results were compared to those obtained using a novel Nave Bayes classifier with accuracy: 87.28% for Naive Bayes, and 83.01% for SVM.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Alhijaj,

Khudeyer

2023

IJCAI

View full text Add to dashboard Cite

A fingerprint is a common form of biometric technology used in human identification. The classification of fingerprints is crucial in identification systems because it significantly reduces the time required to identify a person and allows for the possibility of using fingerprints to distinguish between genders and identify individuals. Fingerprints are the most reliable identifiers because they are unique and impossible to fake. As a method of personal identification, fingerprints remain the best and most trustworthy. Fingerprint classification is crucial in a wide variety of settings, such as airports, banks, and emergencies involving explosives and natural disasters. This study proposes a deep learning strategy for determining whether a fingerprint belongs to a male or female person. With the help of pre-trained convolutional neural networks (CNN) in computer vision and an extremely powerful tool that has achieved significant success in image classification and pattern recognition. This work includes the use of the SOCOFing fingerprint dataset for training and employing a state-of-the-art model for feature extraction called EfficientNetB0, which was trained on the ImageNet dataset. Then feeding the extracted features into a principal component analysis (PCA) to decrease the dimension of these features and random forest RF classifier for fingerprint classification. Lastly, the tests showed that the proposed strategy outperformed the previous categorization methods in terms of accuracy (99.91%), speed for execution time, and efficiency.Povzetek: V članku je opisana metoda globokega učenja za ugotavljanje, ki skoraj 100% ugotovi, ali prstni odtis pripada moškemu ali ženski.

show abstract