Image Quality Assessment for Fake BiometricDetection: Application like Iris, Fingerprint,and Face Recognition

OBJECTIVE

Embedded innovation lab provides final year embedded system projects in Chennai. We use the multiple biometric systems to detect different types of fraudulent access attempts.

It should the comparing 25 image quality features extracted from one to distinguish between legitimate and impostor samples.

Example: Biometric application likeIRIS,FACE ,FINGERPRINT,HEART..,Etc. //www.embeddedinnovationlab.com//

Proposed system

To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures.

In this paper, we present a novelsoftware-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent accessattempts.

PROPOSED SYSTEM ADVANTAGE

• The proposed method shows superior performance for multiple biometric detection.

• It is simple involving minimum parameter tuning. Eil gives final year ece projects in chennai,bangalore .

• This method can extract the image better than other companion method.

• HTER values are high.

LITERATURE REVIEW

1.In Paper Javier (Galbally Z.Wei, 2014)introduced a novel software based multi biometric and multi attack protection method which targets to overcome part of imitations through the use of image quality assessment (IQA). It is not only capable of operating with a very good performance under different biometric systems and for diverse spoofing scenarios.

LITERATURE REVIEW

2. The project (Minakshi R. Rajput Z.Wei, 2013) reviewed literature for iris recognition and explains the need and significance of iris images.

3. The paper (Poonam Dabas Z.Wei, 2013), introduced objective methods formeasuring the quality of images.

H/W and S/W based detectiontechniques

SensorFeature

extractor

Biometric

H/W based liveness

detection

s/w Liveness

dedication

Attack: spoofing

Attack: synthetic re contracted samples

Biometric protection method based on Image Quality Assessment

Gaussian Filtering

FR-IQA

NR-IQA

Real/fakeFinal parameterization R/F

Training data

Iris images Top one real ,Bottom one fake

Fake

Real

Iris

For the iris modality the protection method is tested under two different attack scenarios, namely spoofing attack and attack with synthetic samples.

synthetically generated iris samples which are injected in the communication channel between the sensor and the feature extraction module

Face spoofing attack

Fake

Real

Fake

fake

Controlled Adverse

Final year embedded system projects in chennai

FINGER PRINT ANALYSIS

Biometric output

REFERENCES[1] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition:Security and

privacy concerns,” IEEE Security Privacy, vol. 1, no. 2,pp. 33–42, Mar./Apr. 2003.

[2] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,”in Proc. AWB, 2004.

[3] J. Galbally, C. McCool, J. Fierrez, S. Marcel, and J. Ortega-Garcia, “On the vulnerability of face verification systems to hill-climbing attacks,”Pattern Recognit., vol. 43, no. 3, pp. 1027–1038, 2010.

[4] A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,”EURASIP J. Adv. Signal Process., vol. 2008, pp. 113–129, Jan. 2008.

[5] J. Galbally, F. Alonso-Fernandez, J. Fierrez, and J. Ortega-Garcia,“A high performance fingerprint liveness detection method based on quality related features,” Future Generat. Comput. Syst., vol. 28, no. 1,pp. 311–321, 2012.

[6] K. A. Nixon, V. Aimale, and R. K. Rowe, “Spoof detection schemes,”Handbook of Biometrics. New York, NY, USA: Springer-Verlag, 2008,pp. 403–423.

Keywords

Final year ece projects in chennai,bangaloreFinal year embedded system projects in

chennai,bangalore.Final year engineering projects in

chennai,bangalore //www.embeddedinnovationlab.com //