State of Art analysis

1. Politecnico di Milano Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) Biomed Meeting Sara Bridio sara.bridio@mail.polimi.it Thursday, April 7, 2016 Giulia Core coregiulia@gmail.com State of Art Analysis

2. 2 Easy to use Non invasive From any vital subject Accessible to various sites Low cost Less fragile PPG Biometric

3. 3 2003 First PPG recognition methods 2007 PPG derivatives 2011 Automated feature extraction 2013 Continuos authentication Methods 2014 New features- ranking algorithm 2015 Samsung interest 2015 APG

4. 4 Methods: PPG sensor attached to the fingertip Statistical features extraction based Machine learning based Statistical consistency Discriminability Goals: Feature extraction methods Acquisition:

5. 5 [1] Y. Y. Gu, Y. Zhang, and Y . T. Zhang, “A Novel Biometric Approach in HumanVerification by Photoplethysmographic Signals”, 2003 [2] Y. Y. Gu, Y. Zhang, and Y . T. Zhang, “Photopletismographic Authentication trough Fuzzy Logic”, 2003 First approach 17 subjects 94% success Derivatives 3 subjects [3] J. Yao, X. Sun, and Y. Wan, “A Pilot Study on Using Derivatives of Photoplethysmographic Signals as a Biometric Identifier”, 2007 2003 2007

6. 6 2003 2007 [4] P. Spachos, J. Gao and D. Hatzinakos, “Feasability study of photopletysmographic signals for biometric authentication”, 2011 Automated way 2011 2 datasets  14/15 subjects  Automatic features extraction Linear Discriminant Extraction (LDA) Feature extraction tool + Supervised learning methods

7. 7 [4] P. Spachos, J. Gao and D. Hatzinakos, “Feasability study of photopletysmographic signals for biometric authentication”, 2011 OpenSignal PPG Datasets BioSec PPG Datasets Clustering PPG signals must be obtained in a controlled environment and with accurate sensors

8. 8 2003 2007 [5] A. Bonissi, R. D. Labati, L. Perico, R. Sassi, F. Scotti, L. Sparagino, “A preliminary study on continuous athentication methods for phoplethysmographic biometrics”, 2013 Continuous authentication methods 2011 44 subjects 2013 2 min Segmentation 20s, 30s, 40s 14 subjects 15 min Segmention 40s

9. 9 [5] A. Bonissi, R. D. Labati, L. Perico, R. Sassi, F. Scotti, L. Sparagino, “A preliminary study on continuous athentication methods for phoplethysmographic biometrics”, 2013 2 min 15 min Best results with 40s Features time variability Continuous enrollment needed

10. 10 2003 2007 [6] A. R. Kavsaoğlu, K. Polat, M. R. Bozkurt, “A novel feature algorthm for biometric recognition with ”, 2014 A novel feature-ranking algorithm 2011 2013 2014 30 subjects 1st configuration 2nd configuration 3rd configuration

11. 11 2003 2007 [6] A. R. Kavsaoğlu, K. Polat, M. R. Bozkurt, “A novel feature algorthm for biometric recognition with ”, 2014 2011 2013 2014 Feature- ranking (FR) algorithm 1 2 3 … … … 39 40 k- nearest neighborsValidation A novel feature-ranking algorithm 30 subjects  40 features

12. 12 [6] A. R. Kavsaoğlu, K. Polat, M. R. Bozkurt, “A novel feature algorthm for biometric recognition with ”, 2014 k n° features Success with FR (%) Success without FR (%) 1st configuration 1 25 90,44 89,33 2nd configuration 1 20 94,44 90,22 3rd configuration 3 15 87,22 84,22 Best results with Feature-Ranking Poor results considering time evolution

13. 13 2003 2007 [7] A. Lee and Y. Kim, “Photoplethysmography as a form of biometric recognition”, 2015 2011 2013 2014 10 subjects 2015 Feel-forward neural networks PPG biosignals contain uniquely identifiable information PPG sensitivity to physical conditions  22 features http://www.samsung.com

14. 14 [8] K. A. Sidek, N. I. Zainal, S. N. A. M. Azam and N. A. L. Jaafar, “The development of human biometric identification using acceleration plethysmogram”, 2015 APG 2003 2007 2011 2013 2014 2015 Acceleration plethysmogram

15. 15 Year Authors Innovation Methods Subjects Results in recognition 2003 Y. Y. Gu et al. PPG for biometrics Statistical 17 Success 94% 2007 J. Yao et al. PPG derivatives Statistical 3 2011 P. Spachos et al. Automated feature extraction Machine learning 14/15 EER 25% 2013 A. Bonissi et al. Continuous authentication Statistical 44 2 min  EER 5.29% 15 min  EER 13,47% 2014 A. R. Kavsaoğlu et al. Features- ranking algorithm Machine learning 30 Success 94,44% Success 87,22% 2015 A. Lee et al. Samsung’s interest Machine learning 10 Success 95,88% 2015 K. A. Sidek et al. APG Machine learning 10 Success 98%

16. 16 2003 2007 2011 2013 2014 2015 2016 Wearable device ECG-based algorithms Enhance accuracy Strong biometric recognition system

17. coregiulia@gmail.com sara.bridio@mail.polimi.it Emails Facebook Twitter https://www.facebook.com/bioreds.project/ Politecnico di Milano, NECST lab, DEIB, building 20, via Ponzio, 34/5, 20133, Milano https://twitter.com/BioREDs_necst bioreds.necst@gmail.com