Fingerprint Liveness Detection

J.BHARGAVI

  Department of Electronics and Communication  Engineering
Sree visvesvaraya Institute of Technology and Science
			             Javvajibhargavi970@gmail.com

Abstract

“Liveness detection”, a technique used to determine the vitality of a submitted biometric, has been implemented in fingerprint scanners in recent years. The goal for the LivDet 2011 competition is to compare software-based fingerprint liveness detection methodologies (Part 1), as well as fingerprint systems which incorporate liveness detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live fingerprint images. This competition was open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint vitality detection problem. Five submissions across the two parts of the competition resulted in successful completion. These submissions were: Chinese Academy of Sciences Institute of Automation (CASIA), Federico II University (Federico) and Dermalog Identification SystemsGmbH (Dermalog) for Part 1: Algorithms, and GreenBit and Dermalog for Part 2: Systems. Part 1 was evaluated using four different datasets. The best results were from Federico on the Digital Persona dataset with error for live and spoof detection of 6.2% and 11.61% respectively. The best overall results for Part 1 were Dermalog with 34.05 FerrFake and 11.825% FerrLive. Part 2 was evaluated using live subjects and spoof finger casts. The best results were from Dermalog with an error for live and spoof of 42.5% and 0.8%, respectively.

1. Introduction

Biometrics has been an expanding industry in recent years and provides security through identifying a person based on physiological or behavior characteristics.
However, it has been shown that biometric systems are

vulnerable to spoof attacks by artificial fingerprint casts made of materials such as PlayDoh, silicone, or latex.
There have been numerous methods proposed to solve the susceptibility of fingerprint devices to attacks by spoof fingers. One primary countermeasure to spoofing attacks is called “liveness detection.” Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if an image is authentic. Liveness detection uses either a hardware-based system or software-based system coupled with the authentication program to provide additional security. Hardware-based systems use additional sensors to gain measurements outside of the fingerprint image itself to detect liveness. Software-based systems use image processing algorithms to gather information directly from the collected fingerprint to detect liveness. These systems classify images as either live or fake

A standard assessment of fingerprint liveness detection methods has been lacking in the industry by which different organizations can be compared. The First International Fingerprint Liveness Detection Competition – LivDet 2009, provided an initial assessment of software systems based on the fingerprint image only, but did not address into integrated systems. The second Liveness Detection Competition 2011 (LivDet 2011) was created in order to ascertain the current state of the art in liveness detection, including integrated system testing. LivDet 2011 was open to all academic and industrial institutions and contained two parts: evaluation of software-based systems in Part 1: Algorithms, and evaluation of integrated systems in Part 2: Systems.

In this paper, the competition design and results of the submitted algorithms and systems are summarized. Section 2 of this paper describes the background of spoofing and liveness detection. Section 3 delves into the protocol behind the evaluation of algorithms and systems. Section 4

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discusses the results of the competition and conclusions that are drawn from the results.

Figure 1. Negative impression of five fingers using consensual method.

Figure 2. Latex spoof on finger

2. Background

The concept of spoofing has existed for some time now. Research into spoofing can be seen beginning in 1998 from research conducted by D. Willis and M. Lee where six different biometric fingerprint devices were tested against fake fingers and it was found that four of the six were susceptible to spoofing attacks [1]. This research was approached again in 2000-2002 by multiple institutions including; Putte and Kuening as well as Matsumoto (et al.) [2-3]. The research presented by these researchers looked at the vulnerability of spoofing. In 2001, Kallo (et al.) looked at a hardware solution to Liveness Detection while in 2002; Schuckers delved into using software approaches for Liveness Detection [4-5].

There are two general forms of creating artificial fingers, the cooperative method and non-cooperative method. In the cooperative method the subject pushes their finger into a malleable material such as dental impression material, plastic, or wax creating a negative impression of the fingerprint as a mold, see Figure 1. The mold is then filled with a material, such as gelatin, PlayDoh or silicone.

This cast can be used to represent a finger from a live subject, see Figure 2.

The non-cooperative method involves enhancing a latent fingerprint left on a surface, digitizing it through the use of a photograph, and finally printing the negative image on a transparency sheet. This printed image can then be made into a mold, for example, by etching the image onto a printed circuit board which can be used to create the spoof cast.

The challenge to fingerprint recognition systems is the ability to detect if a presented fingerprint is from a live person or an artificial finger. Systems are being upgraded to incorporate liveness detection solutions that will be able to detect if the submitted probe is a spoof or live finger.
Liveness detection can be incorporated into a system through the addition of hardware components to the capture device that can search for traits in the fingerprint through the use of blood pressure, electrocardiogram, temperature or other methods. Liveness detection can also be implemented through the use of algorithms that are added to the system. This method looks to see if there are features inside of the fingerprint images to determine liveness.

There are many solutions that have been proposed to solve the vulnerability of spoofing [6-8]. LivDet 2009 created a benchmark for measuring liveness detection algorithms. It provided results that showed the current state of the art at that time [9]. The objective of this competition is to expand on LivDet 2009 by evaluating the performance of both algorithms and systems using a standardized experimental protocol. The value in the addition of system testing is to assess the performance of liveness detection approaches tuned to a specific commercial device.

3. Experimental Protocol and Evaluation

The competition features two distinct parts; Part 1: Algorithms and Part 2: Systems, with separate protocols designed for each part. Each part contains their own constraints necessary to eliminate the variability that may be present across algorithms or systems. The design of the experiment will be discussed in detail in this section also outlining the constraints placed on entrants for each part.

3.1  Participants

The competition is open to all academic and industrial institutions. Upon registration, each participant is required

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to sign a database release agreement detailing the proper usage of data made available through t he competition. Participants are then given a database access letter with a username and password to access the s erver to download the training data. The fingerprint data and its release to participants were approved by the Institutional Review Board at Clarkson University.

3.2 Part 1: Algorithm Data Set

The dataset for Part 1: Algorithms cons ists of images from four different devices; Biometrika, Digi tal Persona, Italdata and Sagem. There are 4000 images for each of these devices, 2000 live images and 2000 spoof images (400 of each of 5 spoof materials). The spoof materials used for this experiment were gelatine, latex, PlayDoh, silicone and wood glue for Digital Persona and Sag em (400 each) and gelatine, latex, ecoflex (platinum-catalysed silicone), silicone and wood glue for Biometrika and ItalData (400 each)The dataset of 4000 images per sc anner were divided into two equal datasets, training and tes ting. Details are described in Table 1 and Table 2. Live images came from 400 fingers from 50 people for Biometr ika and ItalData datasets, 200 fingers representing 100 p eople for, Digital Persona dataset, and 112 fingers from 5 6 people for Sagem dataset. Spoof images come from appro ximately 100 fingers representing 50 people for the D igital Persona and Sagem Datasets and 81 fingers represe nting 22 subjects for the Biometrika and ItalData datasets.

The spoof images were collected us ing the consensual method that was described earlier. After the competition is completed, the entire dataset will be m ade available to those who sign the proper data release agreement. Figure 3 below shows examples of images used in the experiment.

(a)	(b)	(c)

(d)	(e)	(f

(g)	(h)	(i)

(j)	(k)	(l)

Figure 3. Examples of fake fingerp rint images, from Biometrika

(a)	latex, (b) gelatin, (c) silicone; from Digital Persona: (d) latex,

(e)	gelatin, (f) silicone; from Italda ta: (g) latex (h) gelatin (i) silicone; from Sagem: (j) latex (k) gelatin (l) silicone.

Table 1. Device characteristics for Part 1 datasets

Dataset	Sensor	Model	No.	Resolution	Image Size	
				(dpi)		
#1	Biometrika	FX200 0	500	315x372	
						
#2	Digital	4000	B	500	355x391	
	Persona					
#3	ItalData	ET10		500	640x480	
						
#4	Sagem	MSO3	00	500	352x384	

Table 2. Training and test set for Part 1 datasets

Dataset	Sensor	Live Training	Live	Number	
		Samples	Testing	of	
			Samples	Fingers	
					
#1	Biometrika	100 0	1000	200	
					
#2	Digital	100 0	1000	200	
	Persona				
					
#3	ItalData	100 0	1000	200	
					
#4	Sagem	100 0	1000	112	

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3.3 Algorithm Submission

The algorithm submission for LivDet 2011 uses the same structure as LivDet 2009. As stated for LivDet 2009 each submitted algorithm must be a Win32 console application with the following list of parameters:
LIVENESS_XYZ.exe [ndataset] [inputfile] [outputfile]

Each parameter, specified in Table 3, and related to the data set configuration must be set before submission. Only Win32 console applications with the above characteristics will be accepted for the competition [9].

Table 3. Formats of submission requirements

Arguments	Description	
		
LIVENESS_XYZ.exe	It is the executable name, where XYZ is	
	the identification number of the	
	participant.	
	LIVENESS_XYZ.exe	
	Format : Win32 console application	
	(.exe)	
		
[ndataset]	It is the identification number of the data	
	set to analyse.	
	Legend: 1=Biometrika, 2=Digital	
	Persona, 3=Italdata, 4=Sagem	
		
[inputfile]	Txt file with the List of images to	
	analyse. Each image is in RAW format	
	(ASCII)	
		
[outputfile]	Txt file with the output of each	
	processed image, in the same order of	
	inputfile. The output is a posterior	
	probability of the live class given the	
	image, or a degree of “liveness”	
	normalized in the range 0 and 100 (100	
	is the maximum degree of liveness, 0	
	means that the image is fake).	
	In the case that the algorithm has not	
	been able to process the image, the	
	correspondent output must be -1000	
	(failure to enroll).	

3.4 Part 2: Systems Submission

In this component, participants were asked to ship a fingerprint system which captures a fingerprint image as well as outputs a liveness detection score. Three spoof

recipe and methods were provided upon registration (Play-doh, Gelatin, Silicone). These were used for testing the system. Two additional unspecified methods were also

used in testing (Body Double, Latex). The requirements for installation are that the system will be run on a Windows XP 32-bit system, that the file will be an “.exe” or similar executable and that the system will use either a USB or Firewire connection.
The system is required to output the collected image and a liveness score normalized in the range of 0 and 100 (100 is the maximum degree of liveness, 0 spoof). In the case that the algorithm has not been able to process the image, the correspondent output must be -1000 (failure to enroll).

Laboratory staff systematically attempted to spoof the system and collect corresponding live data. For each submitted system, 500 live attempts from 50 people (5 images each of R1 and R2 fingers) were performed, as well as 750 spoof attempts for five different materials (Play-doh, Gelatin, Silicone, Body Double, and Latex). Gelatin, PlayDoh and Silicone recipes were supplied to the participants. Body Double and Latex were recipes unknown to the participants. This experiment used molds from 25 subjects with 2 fingers each and 3 images per finger per spoof. The same physical spoof fingers were placed on both scanners. A spoof image was collected on one scanner and then the next scanner alternating between the two scanners.

3.5 Performance Evaluation

The parameters adopted for the performance evaluation will be the following:

Evaluation per sensor/system

-	Frej_n: Rate of failure to enroll for the sub-set n.

-	Fcorrlive_n: Rate of correctly classified live fingerprints for sub-set n.
-	Fcorrfake_n: Rate of correctly classified fake fingerprints for sub-set n.
-	Ferrlive_n: Rate of misclassified live fingerprints for sub-set n.

-	Ferrfake_n: Rate of misclassified fake fingerprints for sub-set n.
-	ET: Average processing time per image

.

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4  Results and Discussion

Three algorithm submissions and two system submissions successfully completed the competition at the time of submission of this paper: Dermalog, Federico and CASIA for Part 1, Dermalog and GreenBit for Part 2.

4.1 Part 1: Algorithms

The rate at which each algorithm produced a false rejection of a live subject (Ferrlive) is shown in Figure 4 and summarized in Table 4. The rate at which each algorithm produced a false acceptance of a spoof image is shown in Figure 5 and summarized in Table 4 (FerrFake).
The threshold value for determining liveness was set at

50.	This threshold is used for the values given for Ferrfake and FerrLive. The liveness values were relatively varied and thus in many cases, changing the threshold would have an effect on the false accept and false reject rates. Figure 6 shows the change in error rates as a function of the threshold for all algorithms across all datasets and Figure 7 for the Sagem Dataset.

FerrLive for Algorithms

Live Called Fake

70																										
																										
50																										
																										
30																								Dermalog		
10																										
-10																								Federico		

 CASIA

Figure 4: FerrLive (%) for each dataset, (left to right) for two submitted algorithms (Dermalog, Federico).

The best balance of spoof and live detection was the Federico algorithm on the Digital Persona dataset at only

6.2% FerrLive and 11.61% FerrFake. The Sagem dataset had a low FerrLive for all three algorithms. The overall classification rate is 27.2% for CASIA, 25.5% for Federico and 22.9% for Dermalog.

FerrFake for Algorithms

	Fake Called Live		
70			
50			
30	Dermalog		
10	Federico		
-10			
	CASIA		

Figure 5. FerrFake (%) for each dataset (left to right) for two submitted algorithms (Dermalog, Frederico)

Algorithm (columns)	Dermalog	Federico	CASIA	
Datasets (rows)	FerrLive	FerrLive	FerrLive	
				
Biometrika	11%	38%	29.7%	
				
ItalData	15.10%	39.90%	50.6%	
				
Sagem	15.10%	13.80%	22.1%	
				
Digital Persona	66%	6.20%	16.1%	
				
Average	35.30%	26.60%	29.625%	

Algorithm (columns)	Dermalog	Federico	CASIA	
Datasets (rows)	FerrFake	FerrFake	FerrFake	
				
Biometrika	29%	42%	38.1%	
				
ItalData	28.50%	40.10%	2.8%	
				
Sagem	12.50%	13.10%	23.6%	
				
Digital Persona	6.20%	11.60%	34.7%	
				
Average	17.60%	24.50%	24.8%	

Table 4: FerrFake and FerrLive for submitted algorithms

Biometrika and ItalData both had high FerrFake rates for Dermalog and Federico algorithms. FerrLive rate does not vary for Dermalog algorithm, whilst it gets worse for Federico algorithm. This can be due to several reasons, which should be better investigated in a future paper, maybe related to the characteristics of these data sets.

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Equal Error Curve for Dermalog
	100							
	90					FerrFake		
						FerrLive		
								
	80							
	70							
Error	60							
	50							
Percent								
	40							
	30							
	20							
	10							
	0	20	40	60	80	100		
	0							
				Threshold				

Equal Error Curve for Federico
	100												
										FerrFake			
	90									FerrLive			
													
	80												
	70												
Error	60												
	50												
Percent													
	40												
	30												
	20												
	10												
	0	10	20	30	40	50	60	70	80	90	100		
	0												
						Threshold							

Equal Error Curve for CASIA
	100												
										FerrFake			
	90									FerrLive			
													
	80												
	70												
Error	60												
	50												
Percent													
	40												
	30												
	20												
	10												
	0	10	20	30	40	50	60	70	80	90	100		
	0												
						Threshold							

Figure 6. Change in error rate: FerrFake (dashed) and FerrLive (solid) compared to thresholds for both algorithms (Dermalog top, Frederico middle, CASIA bottom) across all datasets

Equal Error Curve for Dermalog Sagem

	100							
	90					FerrFake		
						FerrLive		
								
	80							
	70							
Error	60							
	50							
Percent								
	40							
								
	30							
	20							
	10							
	0	20	40	60	80	100		
	0							
				Threshold				

Equal Error Curve for Federico Sagem

	100							
	90					FerrFake		
						FerrLive		
								
	80							
	70							
Error	60							
	50							
Percent								
	40							
	30							
	20							
	10							
	0	20	40	60	80	100		
	0							
				Threshold				

Equal Error Curve CASIA Sagem
	100												
										FerrLive			
	90									FerrFake			
													
	80												
	70												
Error	60												
	50												
Percent													
	40												
	30												
	20												
	10												
	0	10	20	30	40	50	60	70	80	90	100		
	0												
						Threshold							

Figure 7. Change in error rate: FerrLive (solid) and FerrFake (dashed) compared to thresholds for both algorithms (Dermalog top, Federico middle, CASIA bottom) on Sagem Dataset

The failure to enroll rate was 0% for all algorithms on all datasets. The average processing time per image was about ten times lower for Dermalog as Federico and about two times lower than CASIA. Dermalog processed images at an

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average elapsed time of 0.28 seconds per image. Federico processed images at an average elapsed time of approximately 3 seconds per image. CASIA processed images at an average elapsed time of approximately 0.6 seconds.

4.2 Part 2: Systems

FerrLive and FerrFake for the two submitted systems can be found in Figure 8 below with the data summarized in Table 5. Dermalog performed at a FerrLive of 42.5% and a FerrFake of 0.8%. GreenBit performed at a FerrLive of 38.8% and a FerrFake of 39.47%. Both systems had high FerrLive scores.

Error Rates for Submitted Systems

	45			
	40			
%	35			
	30			
Error				
	25			
Percent	20			
	15			
	10			
	5			
	0			
	FerrLive	FerrFake		

Figure 8. FerrLive and FerrFake for submitted systems for Dermalog, GreenBit

Table 5. FerrLive and FerrFake for submitted systems

Submitted	FerrFake	FerrLive	FerrFake	FerrFake	
Systems			Known	Unknown	
					
Dermalog	0.8%	42.5%	0.4%	1.3%	
Greenbit	39.5%	38.8%	19.1%	70%	

Figure 9: FerrFake for unknown vs. known recipes for Dermalog (top) and Greenbit (bottom)

Figure 9 shows the FerrFake rate for both systems for the unknown recipes and known recipes. It can be seen both systems performed better against known recipes.

The software for each system had built-in auto-detect that would allow the system to keep attempting to detect an image until either an image was detected or the scan manually stopped. The frej_n was 0% for both systems because eventually all images were detected.

5  Conclusions

In summary, LivDet 2011 is the second international public competition for software-based fingerprint liveness detection and first public assessment of system-based fingerprint liveness detection. Entries were submitted from

a	total of five participants demonstrating the state-of-the art in fingerprint liveness. LivDet 2011 evaluates (1) software

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approaches applicable to four fingerprint sensors represented in the training data as well (2) embedded hardware/software systems for liveness detection specialized to a specific fingerprint sensor. The best results shown were by Dermalog in Part 1 and Dermalog again in Part 2. It is hoped that this competition will be continued in order to continually understand and promote the state of the art in liveness detection. Results of this competition are not reflective of performance for spoof attacks not included in this study. Creating effective liveness detection methodologies is an important step in minimizing the vulnerability of spoof attacks.

Acknowledgements

We would like acknowledge the funding support from Center for Identification Technology Research (CITeR), from TABULA RASA project, 7th Framework Research Programme of the European Union (EU), grant agreement number: 257289, and from PRIN 2008 project ``Biometric Guards - Electronic guards for protection and security of biometric systems" funded by the Italian Ministry of University and Scientific Research (MIUR).

References

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10.1016/j.patcog.2010.01.023.

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[9]	Gian Luca Marcialis, Aaron Lewicke, Bozhao Tan, Pietro Coli, Fabio Roli, Stephanie Schuckers, Dominic Grimberg, Alberto Congiu, Alessandra Tidu, First International Fingerprint Liveness Detection Competition – LivDet 2009.

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