Study of Deep Learning Methods f or Fingerprint Recognition

Diarra, Mamadou; AYIKPA, Kacoutchy Jean; Bakary, Ballo Abou; Medard, Kouassi Brou; Teacher,

doi:10.35940/ijrte.c6478.0910321

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The F1-score allows for a combination of precision and recall, it gives an on aggregate accuracy of the model with positive and negative predictions. It is a more powerful performance indicator than the accuracy and precision measures, which can be erroneous when the data are highly imbalanced [19]. Its formula is defined as follows:…”

Section: A Datasetmentioning

confidence: 99%

Automatic Identification of Ivorian Plants from Herbarium Specimens using Deep Learning

Bakary¹,

Diarra²,

AYIKPA³

et al. 2022

IJETAE

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— Plant identification is most often based on visual observations by botanists and systematists. Deep learning has become a tool that provides an alternative to automatic plant identification. Our study consists in implementing a method for plant recognition from herbarium specimens using deep learning classification methods. These methods were evaluated on the dataset of ten plant families from the national herbarium of Côte d'Ivoire. The proposed work uses CNN architectures such as DensNet-121, InceptionV3, VGG19, MobileNet, and ResNet101. The dataset contains 7543 images of herbarium specimens. The database is structured in three parts: training, testing, and validation. The accuracies obtained for the first scenario without preprocessing of herbarium specimen images are 76.94% for MobileNet, 77.77% for VGG19, and 77.96% for InceptionV3, 80.41% for ResNet101, and 83.47% for DensNet-121, respectively. The best performance was obtained with DensNet-121 with 83.47%. In the second scenario with preprocessing of herbarium specimens, the accuracies obtained were 82.80% for InceptionV3, 84.40% for VGG19, 85.53% for MobileNet, and 85.80% for ResNet101. The best accuracy was obtained with ResNet121 with 85.80%. From the analysis obtained, the results show that ResNet101 gives the best accuracy compared to the other architectures. In particular, the data preprocessing improves the prediction results, of the Convolutional Neural Network algorithms. Keywords— Deep learning, Herbarium specimens, image preprocessing, Convolutional Neural Network, Classification.

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Section: A Datasetmentioning

confidence: 99%

Automatic Identification of Ivorian Plants from Herbarium Specimens using Deep Learning

Bakary¹,

Diarra²,

AYIKPA³

et al. 2022

IJETAE

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“…Moreover, the pictures' or fingerprint alignment data's resolution should be adequate to accommodate augmentation operations like translation, rotation, or skin distortion. The ability to extract fine details may also be influenced by background noise and image rotation [2].…”

Section: Introductionmentioning

confidence: 99%

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Alhijaj,

Khudeyer

2023

IJCAI

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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.

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Fingerprint Recognition Revolutionized: Harnessing the Power of Deep Convolutional Neural Networks

Habib,

Ibrahim

2024

Lecture Notes in Networks and Systems

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Study of Deep Learning Methods f or Fingerprint Recognition

Cited by 6 publications

References 20 publications

Automatic Identification of Ivorian Plants from Herbarium Specimens using Deep Learning

Automatic Identification of Ivorian Plants from Herbarium Specimens using Deep Learning

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Fingerprint Recognition Revolutionized: Harnessing the Power of Deep Convolutional Neural Networks

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