Template protection techniques are used within biometric systems in order to protect the stored biometric
template against privacy and security threats. A great portion of template protection techniques are based
on extracting a key from or binding a key to a biometric sample. The achieved protection depends on the
size of the key and its closeness to being random. In the literature it can be observed that there is a large
variation on the reported key lengths at similar classification performance of the same template protection
system, even when based on the same biometric modality and database. In this work we determine the analytical
relationship between the system performance and the theoretical maximum key size given a biometric source
modeled by parallel Gaussian channels. We consider the case where the source capacity is evenly distributed
across all channels and the channels are independent. We also determine the effect of the parameters such as
the source capacity, the number of enrolment and verification samples, and the operating point selection on the
maximum key size. We show that a trade-off exists between the privacy protection of the biometric system and
its convenience for its users.
In the biometric verification system of a smart gun, the
rightful user of the gun is recognized based on grip-pattern recognition.
It was found that the verification performance of grip-pattern
recognition degrades strongly when the data for training and testing
the classifier, respectively, have been recorded in different sessions.
The major factors that affect the verification performance of this system
are the variations of pressure distribution and hand position
between the probe image and the gallery image of a subject. In this
work, three methods are proposed to reduce the effect of the variations
by using different sessions for training, image registration, and
classifier fusion. Based on these methods, the verification results
are significantly improved.
This paper demonstrates the feasibility of a new method of hand-geometry recognition based on parameters derived from the contour of the hand. The contour is completely determined by the black-and-white image of the hand and can be derived from it by means of simple image-processing techniques. It can be modelled by parameters, or features, that can capture more details of the shape of the hand than what is possible with the standard geometrical features used in hand-geometry recognition. The set of features considered in this paper consists of the spatial coordinates of certain landmarks on the contour. The feature set and the recognition method used are discussed in detail. The usefulness of the proposed feature set is evaluated experimentally in a verification context. The verification performance obtained with contour-based features is compared with the verification performance of other methods described in the literature.
KEYWORDS: Principal component analysis, Sensors, Biometrics, Control systems, Detection and tracking algorithms, Analog electronics, Matrices, Weapons, Resistors, Dimension reduction
This paper describes the design, implementation and evaluation of a user-verification system for a smart gun,
which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 × 44 piezoresistive
elements is used to measure the grip pattern. An interface has been developed to acquire pressure images
from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification
algorithm, which is currently implemented in software on a PC. The verification algorithm is based on a likelihoodratio
classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern
recognition for biometric verification.
Conference Committee Involvement (3)
Biometric and Surveillance Technology for Human and Activity Identification XII
22 April 2015 | Baltimore, MD, United States
Biometric and Surveillance Technology for Human and Activity Identification XI
8 May 2014 | Baltimore, MD, United States
Biometric and Surveillance Technology for Human and Activity Identification X
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