Wyniki wyszukiwania - BazTech

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Effective persons identification using two- and three-dimensional finger knuckles

Bouchareb Faouzi, Samai Djamel, Korichi Maarouf, Meraoumia Abdallah, Lachouri Abederrazak

Przegląd Elektrotechniczny

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2023

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R. 99, nr 6

62--68

EN

Because of their high level of precision, biometric systems continue to attract the attention of several researchers. Different biometric traits have been investigated for use in security systems, such as fingerprints, faces, irises, palmprints, and knuckle prints. In most cases, bi-dimensional information is utilized. To achieve this aim, we have examined the performance of biometric identification systems based on a 3D-FKP database through five pre-trained networks such as AlexNet, VGG19, GoogleNet, ResNet50, and DenseNet201. The obtained experimental results illustrate the effectiveness of the suggested approach, with a high recognition rate and accuracy.

PL

Ze względu na wysoki poziom precyzji systemy biometryczne nadal przyciągają uwagę wielu badaczy. Zbadano różne cechy biometryczne pod kątem wykorzystania w systemach bezpieczeństwa, takie jak odciski palców, twarze, tęczówki, odciski dłoni i odciski kostek. W większości przypadków wykorzystuje się informacje dwuwymiarowe. Aby osiągnąć ten cel, zbadaliśmy wydajność systemów identyfikacji biometrycznej opartych na bazie danych 3D-FKP za pośrednictwem pięciu wstępnie wyszkolonych sieci, takich jak AlexNet, VGG19, GoogleNet, ResNet50 i DenseNet201. Uzyskane wyniki eksperymentalne ilustrują skuteczność zaproponowanego podejścia, przy wysokim współczynniku rozpoznawania i dokładności.

2

Learning-free deep features for multispectral palm-print classification

Aounallah Asma, Meraoumia Abdallah, Bendjenna Hakim

Computer Science

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2023

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T. 24 (2)

243--271

EN

The feature-extraction step is a major and crucial step in analyzing and understanding raw data, as it has a considerable impact on system accuracy. Despite the very acceptable results that have been obtained by many handcrafted methods, these can unfortunately have difficulty representing features in the cases of large databases or with strongly correlated samples. In this context, we attempt to examine the discriminability of texture features by proposing a novel, simple, and lightweight method for deep feature extraction to characterize the discriminative power of different textures. We evaluated the performance of our method by using a palm print-based biometric system, and the experimental results (using the CASIA multispectral palm--print database) demonstrate the superiority of the proposed method over the latest handcrafted and deep methods.

3

Data analysis-based time series forecast for managing household electricity consumption

Bezzar Nour El-Houda, Laimeche Lakhdar, Meraoumia Abdallah, Houam Lotfi

Demonstratio Mathematica

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2022

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Vol. 55, nr 1

900--921

EN

Recently, electricity consumption forecasting has attracted much research due to its importance in our daily life as well as in economic activities. This process is seen as one of the ways to manage future electricity needs, including anticipating the supply-demand balance, especially at peak times, and helping the customer make real-time decisions about their consumption. Therefore, based on statistical techniques (ST) and/or artificial intelligence (AI), many forecasting models have been developed in the literature, but unfortunately, in addition to poor choice of the appropriate model, time series datasets were used directly without being seriously analyzed. In this article, we have proposed an efficient electricity consumption prediction model that takes into account the shortcomings mentioned earlier. Therefore, the database was analyzed to address all anomalies such as non-numeric values, aberrant, and missing values. In addition, by analyzing the correlation between the data, the possible periods for forecasting electricity consumption were determined. The experimental results carried out on the Individual Household Electricity Power Consumption dataset showed a clear superiority of the proposed model over most of the ST and/or AI-based models proposed in the literature.