Name
Abstract | ||
|---|---|---|
In this paper, we address the issue of producing cancelable biometric templates; a necessary feature in the deployment of any biometric authentication system. We propose a novel scheme that encrypts the training images used to synthesize the single minimum average correlation energy filter for biometric authentication. We show theoretically that convolving the training images with any random convolution kernel prior to building the biometric filter does not change the resulting correlation output peak-to-sidelobe ratios, thus preserving the authentication performance. However, different templates can be obtained from the same biometric by varying the convolution kernels thus enabling the cancelability of the templates. We evaluate the proposed method using the illumination subset of the CMU pose, illumination, and expressions (PIE) face dataset. Our proposed method is very interesting from a pattern recognition theory point of view, as we are able to 'encrypt' the data and perform recognition in the encrypted domain that performs as well as the unencrypted case, regardless of the encryption kernel used; we show analytically that the recognition performance remains invariant to the proposed encryption scheme, while retaining the desired shift-invariance property of correlation filters. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICPR.2004.1334679 | ICPR (3) |
Keywords | Field | DocType |
authentication performance,biometric filter,minimum average correlation energy,face recognition,illumination subset,biometric authentication,cancelable biometric template,convolution,shift invariance property,face dataset,training image,cancelable biometric templates,correlation theory,correlation filter,biometric authentication system,proposed encryption scheme,biometrics (access control),training image encryption,cancelable biometric filters,filtering theory,pattern recognition theory point,correlation filters,random convolution kernels,pattern recognition theory,shift invariant,pattern recognition | Kernel (linear algebra),Computer vision,Facial recognition system,Signature recognition,Authentication,Pattern recognition,Convolution,Computer science,Encryption,Artificial intelligence,Biometrics,Kernel (image processing) | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-2128-2 |
Citations | PageRank | References |
112 | 5.45 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marios Savvides | 1 | 1485 | 112.94 |
| Kumar, B.V.K.V. | 2 | 153 | 8.59 |
| Khosla, P.K. | 3 | 931 | 123.84 |