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Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
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Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Category : Image Processing


Sub Category : BIOMETRICS


Project Code : IMP06


Project Abstract

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

Liveness detection methods are usually classified into two ,

          Hardware-based techniques, which add some specific device to the sensor in order to detect particular properties of a living trait.

          Software-based techniques, in this case the fake trait is detected once the sample has been acquired with a standard.

PROPOSED CONCEPT:

          Deep Learning in several vision tasks and by the ability of the technique to leverage data, we focus on two general-purpose approaches to build image-based anti-spoofing systems with convolution networks for several attack types in three biometric modalities, namely iris, face, and fingerprint.

          The first technique that we explore is hyper parameter optimization of network architectures that we henceforth call architecture optimization, while the second lies at the core of convolution networks and consists of learning filter weights via the well-known back-propagation algorithm, hereinafter referred to as filter optimization.

EXISTING TECHNIQUE:

          IMAGE QUALITY ASSESSMENT ALGORITHM

PROPOSED ALGORITHM:

          CONVOLUTIONAL NETWORK OPERATIONS 

TECHNIQUE DEFINITION:

          The use of image quality assessment for liveness detection is motivated by the assumption that: “It is expected that a fake image captured in an attack attempt will have different quality than a real sample acquired in the normal operation scenario for which the sensor was designed.

ALGORITHM DEFINITION:

          Our networks use classic convolution operations that can be viewed as linear and non-linear image processing operations. When stacked, these operations essentially extract higher level representations, named multiband images, whose pixel attributes are concatenated into high- dimensional feature vectors for later pattern recognition

DRAWBACKS:

          On fraudulently access the biometric system.

         The usual digital protection mechanisms (e.g., encryption, digital signature or watermarking) are not effective.

ADVANTAGES:

          Speed and very low complexity, which makes it very well suited to operate on real scenarios.

         Computation load needed for image processing purpose is much reduced, combined with very simple classifiers.

         It takes less computation time.


 
 
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