A Novel Fake Fingerprint Detection Algorithm Using A Triple-Wavelengths Computation
Method Based On Heart Rate Variability (HRV)
UnionCommunity Research Center, 127, Beobwon-ro, Songpa-Gu, 12F, Daemyung Valeon Bldg., Seoul, 05836, Republic of Korea;∗Young-Hyun Baek, Seock-Han Kim, Dong-Ho Lee, Byunggeun Kim, Sun-Dong Kim
e-mail: neural76@unioncomm.co.kr

Abstract. Biometric systems are an emerging technology that enables the authentication of an individual based on
physiological characteristics including face, fingerprint, iris, hand geometry, palm, or behavioral characteristics including
voice, gait, keystroke dynamic and handwriting signature, etc. There number attacks and there remedial solutions discussed
in the literature on different modules of biometrics system and communication links among them. But still the researchers
are not able to secure every module of a biometric system against these attacks[1~3]. Recently, research has shown that it ispossible to spoof a variety of fingerprint scanners using some simple techniques with molds made from paper, rubber, siliconor gelatin materials. In this paper, we propose a new algorithm that detect fake fingerprints by acquiring the properties ofthree inherent wavelengths from the heart rate variability using the red, green and infrared LEDs, and performing composite
computation. To accomplish this goal, we begin with a heart rate variability factors and a waveform acquisition device
design method to construct these components with data signal processing. The next step measures the peak change in heartrate. The peak of the blood flow changes according to the heart rate variability. It is analyzed using red, green, and infrared
LED wavelengths. At this time, the acquired data is not a heart rate of the owner. It is the acquired data of absorbed &reflected wavelength in the real human skin and the blood flow changes. In case of real fingerprints, the average IR LED
data is 38,823 PPG signal, red led data 40,996 PPG signal, green led data 5,570 PPG signal. Fake fingerprints show a big
difference for each material. The silicon material fake fingerprints indicate IR LED 8,146 PPG signal, red led 10,186 PPGsignal, green led 1,289 PPG signal. The gelatin material fake fingerprints are IR LED 16,851 PPG signal, red led 13,726
PPG signal, green led 2,690 PPG signal. The film material fake fingerprints indicate IR LED 7,566 PPG signal, red led 9,523
PPG signal, green led 908 PPG signal. Then, the obtained data is computed in units of 1 second. And performs a task ofconverting analog data into digital data. The next step is to compare the wavelengths of the enrolled real fingerprint with thatof the detected fake fingerprint. Finally, compare the properties difference with the learned fake fingerprint materialwavelengths. When the learning error rate threshold value is exceeded, the computation is completed. Simulations show thatthe proposal algoritm has an effective performance on the results of the fake fingerprint detection rate.
Key words: heart rate variability (HRV), fake fingerprint, biometric, spoof recognition, signal processing,pattern analysis[Test References 1. The various materials of fake fingerprint]

Slicon 1

Slicon 2

Gelratin 1

Gelratin 2

Filim[Test References 2. Simulation test result][ References ][1]

Bozhac Tan and Stephanie Schuckers, “Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise”, PatternRecognition 2010, vol. 43, pp. 2845-2857.

[2]Young-Hyun Baek, Byunggeun Kim and Seock-Han Kim, “ Fake Fingerprint Detection Biometric System Using
Algorithm”, 2018, 7th International Conference on Knowledge Discovery ICNIT 2018, pp 17.

Neural Network[3] S. S Kulkarni and H. Y Patil, “Survey on Fingerprint Spoofing, Detection Techniques and Databases”, IJCA Proceedings on National
Conference on Advances in Computing, 2015, pp. 30-33.