Deep Learning Base Face Anti Spoofing -Convolutional Restricted Basis Neural Network Technique

Abstract

Face anti-spoofing is the practice of using an authorized person's face to escape false face verification with a picture, video, mask, or another substitute. Latest innovations in face recognition and deep learning technology have made it possible to identify people from a distance using images acquired by a camera. The existing methods used different training sets and classifiers to recognize the face, which was difficult to classify and increase the time. A novel technology 3D Convolutional Restricted Basis Neural Network (CRBNN) is proposed in this paper to overcome this drawback. The proposed system is initialized by testing based on cross-entropy loss with augmented facial samples and further reinforced with an explicitly built generalization loss. The 3D-Convolutional layer is used for mapping the feature of the input image. Next, the Boltzmann technique is used for translation to form the reconstructed parameters. The Radial Basis Function Network (RBFN) is used to obtain an accurate classification. The experimental result proved the proposed CRBNN is given a better accuracy rate, less Equal Error Rate, and less time duration than existing methods. Our method is compared with spatial pyramid coding micro-texture (SPMT), Convolutional Neural Network (CNN), and radial basis function network (RBFN) and outperformed.

Keywords:

• Convolutional restricted basis neural network
• Cross-entropy loss
• Face anti-spoofing
• Radial basis function network

Additional information

Notes on contributors

Kasetty Lakshminarasimha

Kasetty Lakshminarasimha is a research scholar in Information and Communication Engineering Department in Anna University. He has a total teaching experience of 8 years. His Area of research is image processing. He has completed his UG-BTech (Electronics & Communication Engineering) in the year 2007 From JNT University, Hyderabad. He has completed his PG-MTech (VLSI System Design) in the year 2012 from JNT University, Hyderabad.

V. Ponniyin Selvan

V Ponniyin Selvan is a research supervisor in Information and Communication Engineering Department in Anna University. He is currently working as professor in Faculty of Electronics and Communication Engineering at Mahendra College of Engineering, Salem, Tamil Nadu. He has a total teaching experience of 17 years. Area of research is networks, image processing. Completed ME in the applied electronics in the year 2005 from Anna University. Completed PhD in the networks in the year 2014 from Anna University. Email: [email protected]