D8876118419

Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks
Jesslin Melba N V¹, Poornima U², Blessy J^3
1JesslinMelba N V*, Department of Electronics and Communication Engineering, Narayanaguru college of Engineering, Manjalumoodu, India.
²Poornima U., Department of Electronics and Communication Engineering, Narayanaguru college of Engineering, Manjalumoodu, India.
³Blessy J, Department of Electronics and Communication Engineering, Narayanaguru college of Engineering ,Manjalumoodu,
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3309-3314 | Volume-8 Issue-5, January 2020. | Retrieval Number: D8876118419/2020©BEIESP | DOI: 10.35940/ijrte.D8876.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.
Keywords: Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN).
Scope of the Article: Deep  Learning.