Contact-Free Heartbeat Signal for Human Identification and Forensics

  • Kamal Nasrollahi
  • Mohammad A. Haque
  • Ramin Irani
  • Thomas B. Moeslund
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The heartbeat signal, which is one of the physiological signals, is of great importance in many real-world applications, for example, in patient monitoring and biometric recognition. The traditional methods for measuring such this signal use contact-based sensors that need to be installed on the subject’s body. Though it might be possible to use touch-based sensors in applications like patient monitoring, it will not be that easy to use them in identification and forensics applications, especially if subjects are not cooperative. To deal with this problem, recently computer vision techniques have been developed for contact-free extraction of the heartbeat signal. We have recently used the contact-free measured heartbeat signal, for biometric recognition, and have obtained promising results, indicating the importance of these signals for biometrics recognition and also for forensics applications. The importance of heartbeat signal, its contact-based and contact-free extraction methods, and the results of its employment for identification purposes, including our very recent achievements, are reviewed in this chapter.


Autism Spectrum Disorder Discrete Cosine Transform Independent Component Analysis Fiducial Point Computer Vision Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ehrlich D, Carey L, Chiou J, Desmarais S, El-Difrawy S, Koutny L, Lam R, Matsu-daira P, Mckenna B, Mitnik-Gankin L, ONeil T, Novotny M, Srivastava A, Streechon P, Timp W (2002) MEMS-based systems for DNA sequencing and forensics. In: Proceedings of IEEE Sensors, vol 1, pp 448–449Google Scholar
  2. 2.
    Lin WS, Tjoa SK, Zhao HV, Liu KJR (2009) Digital image source coder forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 4(3):460–475CrossRefGoogle Scholar
  3. 3.
    Roussev V (2009) Hashing and data fingerprinting in digital forensics. IEEE Secur Priv 8(2):49–55CrossRefGoogle Scholar
  4. 4.
    Peacock C, Goode A, Brett A (2004) Automatic forensic face recognition from digital images. Sci Justice 44(1):29–34CrossRefGoogle Scholar
  5. 5.
    Jain AK, Unsang P (2009) Facial marks: soft biometric for face recognition. In: 16th IEEE international conference on image processing (ICIP), pp 37–40Google Scholar
  6. 6.
    Unsang P, Jain AK (2010) Face matching and retrieval uses soft biometrics. IEEE Trans Inf Forensics Secur 3:406–415Google Scholar
  7. 7.
    Han H, Otto C, Liu X, Jain A (2014) Demographic estimation from face images: human versus machine performance. IEEE Trans Pattern Anal Mach Intell 99Google Scholar
  8. 8.
    Jain AK, Klare B, Unsang P (2011) Face recognition: some challenges in forensics. In: 2011 IEEE international confer-ence on automatic face gesture recognition and workshops (FG 2011), pp 726–733Google Scholar
  9. 9.
    Wagner J, Jonghwa K, Andre E (2005) From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: IEEE international conference on multimedia and expo, pp 940–943Google Scholar
  10. 10.
    Li L, Chen J (2006) Emotion recognition using physiological signals. Adv Artif Reality Tele-Existence Lect Notes Comput Sci Springer 4282:437–446CrossRefGoogle Scholar
  11. 11.
    Kuriakose S, Sarkar N, Lahiri U (2012) A step towards an intelligent Human Computer Interaction: Physiology-based affect-recognizer. In: 4th international conference on intelligent human computer interaction (IHCI), pp 1–6Google Scholar
  12. 12.
    Liao W, Zhang W, Zhu Z, Ji Q (2005) A real-time human stress monitoring system using dynamic Bayesian network. In: IEEE computer society conference on computer vision and pattern recognition—workshopsGoogle Scholar
  13. 13.
    Zhai J, Barreto A (2006) Stress detection in computer users through non-invasive monitoring of physiological signals. Biomed Sci Instrum 42:495–500Google Scholar
  14. 14.
    Barreto A, Zhai J, Adjouadi M (2007) Non-intrusive physiological monitoring for automated stress detection in human-computer interaction. Human-Comput Interact Lect Notes Comput Sci Springer 4796:29–38CrossRefGoogle Scholar
  15. 15.
    Liu C, Torralba A, Freeman WT, Durand F, Adelson EH (2005) Motion magnification. ACM Trans Graph 24(3):519–526CrossRefGoogle Scholar
  16. 16.
    Wu HY, Rubinstein M, Shih E, Guttag J, Durand F, William TF (2012) Eulerian video magnification for revealing subtle changes in the world. In: Proceedings of SIGGRAPHACM transactions on graphics, vol 31, no 4Google Scholar
  17. 17.
    Balakrishnan G, Durand F, Guttag J (2013) Detecting pulse from head motions in video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3430–3437Google Scholar
  18. 18.
    Irani R, Nasrollahi K, Moeslund TB (2014) Improved pulse detection from head motions using DCT. In: 9th International Conference on Computer Vision Theory and Applications, vol 3, pp 118–124Google Scholar
  19. 19.
    Poh MZ, McDuff DJ, Picard R (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express 18:10762–10774CrossRefGoogle Scholar
  20. 20.
    Poh MZ, McDuff DJ, Picard R (2011) Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans Biomed Eng 58:7–11CrossRefGoogle Scholar
  21. 21.
    Sarkar A, Abbott AL, Doerzaph Z (2014) Assessment of psychophysiological characteristics using heart rate from naturalistic face video data. In: IEEE interna-tional joint conference on biometrics (IJCB)Google Scholar
  22. 22.
    Tarassenko L, Villarroel M, Guazzi A, Jorge J, Clifton DA, Pugh C (2014) Non-contact video-based vital sign monitoring using ambient light and autoregressive models. Physiol Meas 35:807–831CrossRefGoogle Scholar
  23. 23.
    Li X, Chen J, Zhao G, Pietikainen M (2014) Remote heart rate measurement from face videos under realistic situations. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 4264–4271Google Scholar
  24. 24.
    Soleymani M, Lichtenauer J, Pun T, Pantic M (2012) A multimodal database for affect recognition and implicit tagging. IEEE Trans Affect Comput 3(1):42–55CrossRefGoogle Scholar
  25. 25.
    Biel L, Pettersson O, Philipson L, Wide P (2001) ECG analysis: a new approach in human identification. IEEE Trans Instrum Measur 50(3):808–812CrossRefGoogle Scholar
  26. 26.
    Hoekema R, Uijen GJH, van Oosterom A (2001) Geometrical aspects of the interindividual variability of multilead ECG recordings. IEEE Trans Biomed Eng 48(5):551–559CrossRefGoogle Scholar
  27. 27.
    Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK (2005) ECG to identify individuals. Pattern Recogn 38(1):133–142CrossRefGoogle Scholar
  28. 28.
    Wang Y, Plataniotis KN, Hatzinakos D (2006) Integrating analytic and appearance attributes for human identification from ECG signals, In: Biometrics symposium: special session on research at the biometric consortium conferenceGoogle Scholar
  29. 29.
    Plataniotis KN, Hatzinakos D, Lee JKM (2006) ECG biometric recognition without fiducial detection. In: Biometrics symposium: special session on research at the biometric consortium conferenceGoogle Scholar
  30. 30.
    Singh YN, Gupta P (2008) ECG to individual identification. In: 2nd IEEE international conference on biometrics: theory, applications and systemsGoogle Scholar
  31. 31.
    Fatemian SZ, Hatzinakos D (2009) A new ECG feature extractor for biometric recognition. In: 2009 16th international conference on digital signal processingGoogle Scholar
  32. 32.
    Odinaka I, Po-Hsiang L, Kaplan AD, O’Sullivan JA, Sirevaag EJ, Kristjansson SD, Sheffield AK, Rohrbaugh JW (2010) ECG biometrics: a robust short-time frequency analysis. In: IEEE international workshop on information forensics and security (WIFS)Google Scholar
  33. 33.
    Coutinho DP, Fred ALN, Figueiredo MAT (2010) One-lead ECG-based personal identification using Ziv-Merhav cross parsing. In: 20th international conference on pattern recognition (ICPR), pp 3858–3861Google Scholar
  34. 34.
    Can Y, Coimbra MT, Kumar BVKV (2010) Investigation of human identification using two-lead Electrocardiogram (ECG) signals. In: Fourth IEEE international conference on biometrics: theory applications and systems (BTAS)Google Scholar
  35. 35.
    Islam MS, Alajlan N, Bazi Y, Hichri HS (2012) HBS: A novel biometric feature based on heartbeat morphology. IEEE Trans Inf Technol Biomed 16(3):445–453CrossRefGoogle Scholar
  36. 36.
    Tantawi M, Revett K, Tolba MF, Salem A (2012) A novel feature set for deployment in ECG based biometrics. In: 7th international conference on computer engineering systems (ICCES), pp 186–191Google Scholar
  37. 37.
    Wang J, She M, Nahavandi S, Kouzani A (2013) Human identification from ECG signals via sparse representation of local segments. IEEE Sign Proces Lett 20(10):937–940CrossRefGoogle Scholar
  38. 38.
    Fratini A, Sansone M, Bifulco P, Romano M, Pepino A, Cesarelli M, D’Addio G (2013) Individual identification using electrocardiogram morphology. In: 2013 IEEE international symposium on medical measurements and applications proceedings (MeMeA), pp 107–110Google Scholar
  39. 39.
    Rabhi E, Lachiri Z (2013) Biometric personal identification system using the ECG signal. In: Computing in cardiology conference (CinC), pp 507–510Google Scholar
  40. 40.
    Ming L, Xin L (2014) Verification based ECG biometrics with cardiac irregular conditions using heartbeat level and segment level information fusion. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3769–3773Google Scholar
  41. 41.
    Lourenco A, Carreiras C, Silva H, Fred A (2014) ECG biometrics: A template selection approach. In: IEEE international symposium on medical measurements and applications (MeMeA)Google Scholar
  42. 42.
    Nomura R, Ishikawa Y, Umeda T, Takata M, Kamo H, Joe K (2014) Biometrics authentication based on chaotic heartbeat waveform, In: Biomedical engineering international conference (BMEiCON)Google Scholar
  43. 43.
    Haque MA, Nasrollahi K, Moeslund TB (2015) Heartbeat signal from facial video for biometric recognition. In: Proceedings of 19th Scandinavian conference on image analysisGoogle Scholar
  44. 44.
    Hegde C, Prabhu HR, Sagar DS, Shenoy PD, Venugopal KR, Patnaik LM (2011) Heartbeat biometrics for human authentication. SIViP 5(4):485–493CrossRefGoogle Scholar
  45. 45.
    Van de Haar H, Van Greunen D, Pottas D (2013) The characteristics of a biometric. In: Information security for South AfricaGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kamal Nasrollahi
    • 1
  • Mohammad A. Haque
    • 1
  • Ramin Irani
    • 1
  • Thomas B. Moeslund
    • 1
  1. 1.Visual Analysis of People (VAP) LaboratoryAalborg UniversityAalborgDenmark

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