Muhammad, U., Holmberg, T., Carneiro de Melo, W., Hadid, A., Face anti-spoofing via sample learning based recurrent neural network (RNN), The British Machine Vision Conference 2019 (BMVC) 9th-12th September 2019, Cardiff UK, p. 1-12
Face anti-spoofing via sample learning based recurrent neural network (RNN)
|Author:||Muhammad, Usman1; Holmberg, Tuomas1; Carneiro de Melo, Wheidima1;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020041516593
British Machine Vision Association Press,
|Publish Date:|| 2020-04-15
Face biometric systems are vulnerable to spoofing attacks because of criminals who are developing different techniques such as print attack, replay attack, 3D mask attack, etc. to easily fool the face recognition systems. To improve the security measures of biometric systems, we propose a simple and effective architecture called sample learning based recurrent neural network (SLRNN). The proposed sample learning is based on sparse filtering which is applied for augmenting the features by leveraging Residual Networks (ResNet). The augmented features form as a sequence, which are fed into a Long Short-Term Memory (LSTM) network for constructing the final representation. We show that for face anti-spoofing task, incorporating sample learning into recurrent structures learn more meaningful representations to LSTM with much fewer model parameters. Experimental studies on MSU and CASIA dataset demonstrate that the proposed SLRNN has a superior performance than state-of-the-art methods used now.
|Pages:||1 - 12|
The British Machine Vision Conference 2019 (BMVC) 9th-12th September 2019, Cardiff UK
British Machine Vision Conference
|Type of Publication:||
D3 Professional conference proceedings
|Field of Science:||
113 Computer and information sciences
The financial support of the Academy of Finland is acknowledged.
© 2019. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.