Implementation methods
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In these slides we give you a detailed explanation of what we have to do in order to build a robust biometric recognition system.
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Implementation methods
- 1. Politecnico di Milano Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) Biomed Meeting Sara Bridio sara.bridio@mail.polimi.it Thursday, May 12, 2016 Giulia Core giulia.core@mail.polimi.it Implementation methods
- 2. Scenario 2 Photoplethysmography Biometric recognition PPG signal from subject 1 PPG signal from subject 2
- 4. 4 Preprocessing fir1( #coefficients , f_cutoff_norm ) filtfilt( filter, 1 , signal ) Filtering FIR filter f_cutoff = 8 Hz Signal frequency 0,001-2 Hz Low pass filter
- 5. 5 Preprocessing Peak detection algorithm AMPD [1] Segmentation 256 samples for each segment Resample [1] F. Scholkmann, J. Boss and M. Wolf, “An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals“, 2012 Drift elimination Segments of PPG signal from subject 1 Segments of PPG signal from subject 1, after drift elimination
- 6. 6 Preprocessing Normalization Normalized segments of PPG signal from subject 1 Segments of PPG signal from subject 1 Segments of PPG signal from subject 1, after drift elimination
- 7. 7 Preprocessing Segments Matrix for each subject Segm Samples 1 2 … 256 Segm 1 Segm n … rows: segments belonging to a subject columns: samples
- 8. 8 Features extraction Template creation PPG signal template Template = mean(segm_mat) Segments matrix Template of PPG signal from subject 1 Template of PPG signal from subject 2
- 9. 9 Features extraction First derivative Second derivative Central finite difference More accurate Less sensitive to noise Matlab function diff(X,ord)
- 10. 10 Features extraction 1st derivative template 2nd derivative template Template of 1st derivative of PPG signals from subject 1 Template of 1st derivative of PPG signals from subject 2 Template of 2nd derivative of PPG signals from subject 2 Template of 2nd derivative of PPG signals from subject 1
- 11. 11 Features extraction Template matching 3 matrixes Euclidean distance 3 matrixes with distances One for each type of template PPG signal template 1st derivative template 2nd derivative template Sum of distances dij One for each type of template i: template subject i j: template subject j
- 12. 12 Features extraction 3 matrixes with distances T1 T2 T1 T2 … … … … Tn Tn 0 0 0 0 0 d12 d12 d1n d1n d2n d2n … … … … … … … …… … … … … … Ti: Template belonging to subject i dij: sum of euclidean distances point to point between Ti and Tj
- 13. 13 Test definition (*) (*) www.angelsensor.com Number of subjects Acquisition time Physiological conditions Stress Physical Acquisition trials
- 14. 14 Evaluation Classificator k-nearest neighbors • Subject 1 Subject 2 Subject 3 Template to assign • k arbitrarily determined • Euclidean distance calculated between and stored data points • Majority ranking on Euclidean distance: the template is assigned to the class with the majority among the k closest templates
- 15. 15 Preprocessing Features extraction Test definition Evaluation Success?no yes Robust recognition system based on PPG signal
- 16. giulia.core@mail.polimi.it 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 http://www.slideshare.net/BioREDsSlideshare
Editor's Notes
- 1
- Da mettere titoli piccoli + Aumenta lunghezza segnale
- Fasi di implementazione
- Guardare a cosa servono i coefficienti!!! Da dire che #coefficient=127? Se coeff. Aumenta aumenta la latenza, ma aumenta la precisione del filtraggio. Per risolvere il problema della latenza usiamo filtfilt? Filtfilt applica segnale avanti e indietro così toglie la latenza Tagliare a metà immagine
- Peak detection noisy periodic-semiperiodic per tutti i segnali Segmentation semplice funzione per selezionare i segmenti utili Resample Normalization
- Peak detection noisy periodic-semiperiodic per tutti i segnali Segmentation semplice funzione per selezionare i segmenti utili Resample Normalization
- Matrice segmenti Per ogni soggetto
- Aggiungere i vantaggi… Eventualmente dire che noi utlizziamo l’ordine di accuratezza 6? Quali sono i coefficienti?
- 3 matrixes righe=segmenti, colonne=campioni
- Spiegare cos’è dij Simmetria Diagonale con zeri
- Da aggiungere: piccola legenda k value arbitrarily determined Euclidean distance between data points referred to different periods Distances ranking: the closest k number of stored templates with the smallest distance are ascertained Among the k number of class labels ascertained, the one with the majority is determined. This determined stored template is assigned as the outcome of the unknown template Determinare k perché la valutazione si fa con i ktemplate più vicini e quindi più simili al nostro template appena acquisito