Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Matching hand radiographs

Biometric verification and identification methods of medical images can be used to find possible inconsistencies in patient records. Such methods may also be useful for forensic research. In this work we present a method for identifying patients by their hand radiographs. We use active appearance model representations presented before [1] to extract 64 shape features per bone from the metacarpals, the proximal, and the middle phalanges. The number of features was reduced to 20 by applying principal component analysis. Subsequently, a likelihood ratio classifier [2] determines whether an image potentially belongs to another patient in the data set. Firstly, to study the symmetry between both hands, we use a likelihood-ratio classifier to match 45 left hand images to a database of 44 (matching) right hand images and vice versa. We found an average equal error probability of 6.4%, which indicates that both hand shapes are highly symmetrical. Therefore, to increase the number of samples per patient, the distinction between left and right hands was omitted. Secondly, we did multiple experiments with randomly selected training images from 24 patients. For several patients there were multiple image pairs available. Test sets were created by using the images of three different patients and 10 other images from patients that were in the training set. We estimated the equal error rate at 0.05%. Our experiments suggest that the shapes of the hand bones contain biometric information that can be used to identify persons

Biometric Boom: How the Private Sector Commodifies Human Characteristics

Biometric technology has become an increasingly common part of daily life. Although biometrics have been used for decades, recent ad- vances and new uses have made the technology more prevalent, particu- larly in the private sector. This Note examines how widespread use of biometrics by the private sector is commodifying human characteristics. As the use of biometrics has become more extensive, it exacerbates and exposes individuals and industry to a number of risks and problems asso- ciated with biometrics. Despite public belief, biometric systems may be bypassed, hacked, or even fail. The more a characteristic is utilized, the less value it will hold for security purposes. Once compromised, a biome- tric cannot be replaced as would a password or other security device. This Note argues that there are strong justifications for a legal struc- ture that builds hurdles to slow the adoption of biometrics in the private sector. By examining the law and economics and personality theories of commodification, this Note identifies market failure and potential harm to personhood due to biometrics. The competing theories justify a reform to protect human characteristics from commodification. This Note presents a set of principles and tools based on defaults, disclosures, incen- tives, and taxation to discourage use of biometrics, buying time to streng- then the technology, educate the public, and establish legal safeguards for when the technology is compromised or fails

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit