Face Spoofing Detection using Enhanced Local Binary Pattern
Karuna Grover1, Rajesh Mehra2

1Karuna Grover, Pursuing Master’s Degree, Electronics and Communication Engineering from National Institute of Technical Teacher’s Training and Research, Chandigarh, India.
2Dr. Rajesh Mehra, Head of Curriculum Development Center National Institute of Technical Teacher Training & Research, Chandigarh, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3365-3371 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3834129219/2019©BEIESP | DOI: 10.35940/ijeat.B3834.129219
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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: Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniques for detection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep features are more robust in comparison to the former one. In this paper, a proper countermeasure in opposite to attacks that are on face spoofing are relied on CNN i.e. Convolutional Neural Network. In this novel approach, deep texture features from images are extracted by integrating the modified version of LBP descriptor (Gene LBP net) to a CNN. Experimental results are obtained on NUAA spoofing database which defines that these deep neural network surpass most of the state-of-the-art techniques, showing good outcomes in context to finding out the criminal attacks.
Keywords: Biometric, Convolutional Neural Networks, Face recognition, Spoofing attacks.