Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement

[1]  Josef Bigün,et al.  Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment , 2007, IEEE Transactions on Information Forensics and Security.

[2]  Yoshinori Kobayashi,et al.  Terrain Recognition for Smart Wheelchair , 2016, ICIC.

[3]  Vladislav Ostankovich,et al.  Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods , 2018, 2018 International Conference on Intelligent Systems (IS).

[4]  Lin Sun,et al.  Blinking-Based Live Face Detection Using Conditional Random Fields , 2007, ICB.

[5]  Sangyoun Lee,et al.  Face Liveness Detection Using Defocus , 2015, Sensors.

[6]  Amir Hussain,et al.  A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair , 2016, Cognitive Computation.

[7]  Al Amin,et al.  Predicting Students’ Performance of the Private Universities of Bangladesh using Machine Learning Approaches , 2020 .

[8]  Ivana Chingovska,et al.  On the Use of Client Identity Information for Face Antispoofing , 2015, IEEE Transactions on Information Forensics and Security.

[9]  Yoshinori Kobayashi,et al.  Autonomous Bus Boarding Robotic Wheelchair Using Bidirectional Sensing Systems , 2018, ISVC.

[10]  Matti Pietikäinen,et al.  Generalized face anti-spoofing by detecting pulse from face videos , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[11]  M. Shamim Kaiser,et al.  Performance Analysis of Optical Wireless Communication System Employing Neuro-Fuzzy Based Spot-Diffusing Techniques , 2013 .

[12]  Ausif Mahmood,et al.  Optimizing Deep CNN Architectures for Face Liveness Detection , 2019, Entropy.

[13]  M. S. Kaiser,et al.  Applying Ant Colony Optimization in software testing to generate prioritized optimal path and test data , 2015, 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[14]  M. Shamim Kaiser,et al.  Low cost and portable patient monitoring system for e-Health services in Bangladesh , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).

[15]  Kang Ryoung Park,et al.  Face liveness detection based on texture and frequency analyses , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[16]  Mufti Mahmud,et al.  Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia , 2020, Brain Informatics.

[17]  Khurram Khurshid,et al.  Deep Face Recognition for Biometric Authentication , 2019, 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE).

[18]  Yaman Akbulut,et al.  Deep learning based face liveness detection in videos , 2017, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP).

[19]  Muhammad Awais,et al.  Spoofing Attack Detection by Anomaly Detection , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Matti Pietikäinen,et al.  Context based face anti-spoofing , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[21]  Haneen Farah,et al.  Heart Rate Analysis for Human Factors: Development and Validation of an Open Source Toolkit for Noisy Naturalistic Heart Rate Data , 2018 .

[22]  Mufti Mahmud,et al.  Advances in Crowd Analysis for Urban Applications Through Urban Event Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.

[23]  Lai-Man Po,et al.  Face liveness detection using convolutional-features fusion of real and deep network generated face images , 2019, J. Vis. Commun. Image Represent..