<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.</p>
</abstract>

Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Department of Computer Science and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of...

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Publié en ligne: 22 sept. 2022

Pages: 127 - 145

Reçu: 08 févr. 2022

Accepté: 22 juil. 2022

DOI: https://doi.org/10.2478/cait-2022-0032

Mots clés
Presentation attack detection, biometrics, computer vision, deep learning, authentication, color-texture analysis

© 2022 Mayank Kumar Rusia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Publié en ligne: 22 sept. 2022

Pages: 127 - 145

Reçu: 08 févr. 2022

Accepté: 22 juil. 2022

DOI: https://doi.org/10.2478/cait-2022-0032

Mots clésPresentation attack detection, biometrics, computer vision, deep learning, authentication, color-texture analysis

© 2022 Mayank Kumar Rusia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Presentation attack detection, biometrics, computer vision, deep learning, authentication, color-texture analysis