Novel Face Liveness Detection Using Fusion of Features and Machine Learning Classifiers
Sudeep D. Thepade, Prasad A. Jagdale, Amit Bhingurde, Shwetali S. Erandole · 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · 2020
As the technology is growing which is birth to different types of frauds into areas like face detection, finger print detection etc. Moreover, it becomes very difficult for service providers to maintain security of data. In addition, these systems needs to be protected from spoofing by attackers. Fraudsters can use dummy Eyes, photographs for identification of faces for authentication purposes. These facial recognitions can also be done through face detection from video streams by replaying videos & capturing specific moments of person. Also these kind of attacks can be done successfully as system can't detect the real life faces & faces extracted from videos & photos. In addition, these videos & photos will be easily available on internet & other stored media, so anyone can challenge the system by using it. Now, many algorithms are implemented to detect face liveness for authentication. The paper represents novel face liveness detection using fusion of luminance-based features with help of assorted machine learning classifiers.