Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images

Alternative to Comprehensive Ecosystem Services Markets: The Contribution of Forest-Related Programs in New Zealand

Due to the public goods characteristics of many ecosystem services and their vital importance to human welfare, various mechanisms have been put in place to motivate private landowners in the provision of ecosystem services. A common approach is to try to develop a comprehensive ecosystem services market where landowners can receive payments from beneficiaries of ecosystem services. Much research has been directed at developing methods for valuing the range of ecosystem services so that they can be incorporated into ecosystem services markets. However, valuation methods are difficult, expensive and time consuming. Other approaches to the provision of ecosystem services such as payments for ecosystem services usually focus on a single service like water or biodiversity. However, in the provision of a particular ecosystem service, there are spill-over effects of providing other ecosystem services, and thus studying those spill-over effects may provide a simple and cost-effective way of ensuring the provision of a wide range of ecosystem services. In New Zealand, there are a variety of forestry programs which provide incentives to landowners to plant trees on their lands to meet particular objectives, but which also produce other ES. This research aims to evaluate the cost-effectiveness of the provision of a wide range of ES by these approaches, the New Zealand Emissions Trading Scheme, the East Coast Forestry Scheme, and the QEII National Trust.ecosystem services market, spill-over effect, cost-effectiveness, New Zealand, Environmental Economics and Policy,

Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud

Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission

DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution

Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems. Previous work by Roy et al. generated synthetic MasterPrints at the feature-level. In this work we generate complete image-level MasterPrints known as DeepMasterPrints, whose attack accuracy is found to be much superior than that of previous methods. The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images. Stochastic search in the form of the Covariance Matrix Adaptation Evolution Strategy is then used to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer. Experiments convey the efficacy of the proposed method in generating DeepMasterPrints. The underlying method is likely to have broad applications in fingerprint security as well as fingerprint synthesis.Comment: 8 pages; added new verification systems and diagrams. Accepted to conference Biometrics: Theory, Applications, and Systems 201

Conjunctival scanning for biometric identification

Researchers at UMKC have developed a biometric device which recognizes the physical characteristics of sclera veins which are visible through the conjunctival membrane in the human eye. The vascular structures of the conjunctiva and episclera are rich with specific details that are useful in identifying individuals. Unlike retinal scans, the vascular structures of the conjunctiva and episclera provide extensive and unique information that can be obtained from various and selected regions of the eye and processed to authenticate or identify individuals. The technology can work with less light, on non-compliant targets, and from much greater distances than currently employed methods. It can function as a stand-alone biometric or could be used in conjunction with exiting ocular-based biometrics to achieve enhanced performance and spoof-proofing. Potential Areas of Applications: * Airport /border security * Law enforcement * Casinos * Private security Patent Status: U.S. Patent no. 7,327,860 Inventor(s): Reza Derakhshani; Arun Ross Contact Info: 0James Brazeal - [email protected] (816) 235-509