SLGP Header

Detecting the Originality of Biometric Details Using Image Quality Assessment

IJCSEC Front Page

Image and biometric details of man is designed artificial by using some software it is called spoofing. Spoofing is one of the most problems in developing security world. In spoofing, process is done by so many software and skilled person. In future, world security is the important one for every person. The biometric data will help in all fields for identification process. So it is needed to develop new method to find and rectify the spoofing data’s. A novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples.
Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization in biometrics. Biometric refers to metrics related to human characteristics and traits. Biometric authentication (realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals .Biometric identifiers are categorized as physiological versus behavioural characteristics. Physiological characteristics are related to the shape of the body. Examples include, but are not limited to finger print, palm veins, Face recognition, Palm Print, Hand Geometry, iris recognition retina. Behavioural characteristics are related to the pattern of behaviour of a person.
Besides other anti-spoofing approaches such as the use of multi biometrics or challenge-response methods, special attention has been paid by researchers and industry to the liveness detection techniques, which use different physiological properties to distinguish between real and fake traits.
Liveness assessment methods represent a challenging engineering problem as they have to satisfy certain demanding requirements. Non-invasive, the technique should in no case be harmful for the individual or require an excessive contact with the user. They are user friendly, people should not be reluctant to use it; fast, results have to be produced in a very reduced interval as the user cannot be asked to interact with the sensor for a long period of time; low cost, a wide use cannot be expected if the cost is excessively high performance, in addition to having a good fake detection rate, the protection scheme should not degrade the recognition performance (i.e., false rejection) of the biometric system
A novel software-based multi-biometric and multi-attack protection method which targets to overcome part of these limitations through the use of image quality assessment (IQA). It is not only capable of operating with a very good performance under different biometric systems (multi-biometric) and for diverse spoofing scenarios, but it also provides a very good level of protection against certain non-spoofing attacks (multi-attack) software-based techniques are in general less expensive (as no extra device is needed), and less intrusive since their implementation is transparent to the user. Furthermore, as they operate directly on the acquired sample (and not on the biometric trait itself), software-based techniques may be embedded in the feature extractor module, which makes them potentially capable of detecting other types of illegal break-in attempts not necessarily classified as spoofing attacks. For instance, software based methods protects the system against the injection of reconstructed or synthetic samples into the communication channel between the sensor and the feature extractor using Image Quality Measures.


  1. Javier Galbally, Sebastien Marcel and Julian Fierrez Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint and Face Recognition “IEEE transactions on image processing, vol. 23, no. 2, February 2014.
  2. 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.
  3. (2012). BEAT: Biometrics Evaluation and Testing [Online]. Available:
  4. A. Hadid, M. Ghahramani, V. Kellokumpu, M. Pietikainen, J. Bustard, and M. Nixon, “Can gait biometrics be spoofed?” in Proc. IAPR ICPR, 2012, pp. 3280–3283.
  5. Z. Akhtar, G. Fumera, G. L. Marcialis, and F. Roli, “Evaluation of serial and parallel multi biometric systems under spoofing attacks,” in Proc. IEEE 5th Int. Conf. BTAS, Sep. 2012, pp. 283–288.
  6. K. Bowyer, T. Boult, A. Kumar, and P. Flynn, Proceedings of the IEEE Int. Joint Conf. on Biometrics. Piscataway, NJ, USA: IEEE Press, 2011.