Learning-free deep features for multispectral palm-print classification

• Autorzy: Aounallah Asma , Meraoumia Abdallah , Bendjenna Hakim

Identyfikatory

DOI

10.7494/csci.2023.24.2.4685

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

feature extraction; biometrics; multispectral imaging; deep learning; DCTNet; data fusion

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (2)
• Strony: 243--271
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Aounallah Asma

• Echahid Cheikh Larbi Tebessi University, Laboratory of Mathematics, Informaticsand Systems (LAMIS), Tebessa, 12002, Algeria

autor

Meraoumia Abdallah

• Echahid Cheikh Larbi Tebessi University, Laboratory of Mathematics, Informaticsand Systems (LAMIS), Tebessa, 12002, Algeria

autor

Bendjenna Hakim

• Echahid Cheikh Larbi Tebessi University, Laboratory of Mathematics, Informaticsand Systems (LAMIS), Tebessa, 12002, Algeria

Bibliografia

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