Shay Ben-David

Biometric authentication on a mobile device: a study of user effort, error and task disruption

We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods.

An innovative low-power high-performance programmable signal processor for digital communications

We describe an innovative, low-power, high-performance, programmable signal processor (DSP) for digital communications. The architecture of this processor is characterized by its explicit design for low-power implementations, its innovative ability to jointly exploit instruction-level parallelism and data-level parallelism to achieve high performance, its suitability as a target for an optimizing high-level language compiler, and its explicit replacement of hardware resources by compile-time practices. We describe the methodology used in the development of the processor, highlighting the techniques deployed to enable application/architecture/compiler/implementation co-development, and the optimization approach and metric used for power-performance evaluation and tradeoff analysis. We summarize the salient features of the architecture, provide a brief description of the hardware organization, and discuss the compiler techniques used to exercise these features. We also summarize the simulation environment and associated software development tools. Coding examples from two representative kernels in the digital communications domain are also provided. The resulting methodology, architecture, and compiler represent an advance of the state of the art in the area of low-power, domain-specific microprocessors.

Multi-modal biometrics for mobile authentication

User authentication in the context of a secure transaction needs to be continuously evaluated for the risks associated with the transaction authorization. The situation becomes even more critical when there are regulatory compliance requirements. Need for such systems have grown dramatically with the introduction of smart mobile devices which make it far easier for the user to complete such transaction quickly but with a huge exposure to risk. Biometrics can play a very significant role in addressing such problems as a key indicator of the user identity and thus reducing the risk of fraud. While unimodal biometrics authentication systems are being increasingly experimented by mainstream mobile system manufacturers (e.g., fingerprint in iOS), we explore various opportunities of reducing risk in a multimodal biometrics system. The multimodal system is based on fusion of several biometrics combined with a policy manager. A new biometric modality: chirography which is based on user writing on multi-touch screens using their finger is introduced. Coupling with chirography, we also use two other biometrics: face and voice. Our fusion strategy is based on inter-modality score level fusion that takes into account a voice quality measure. The proposed system has been evaluated on an in-house database that reflects the latest smart mobile devices. On this database, we demonstrate a very high accuracy multi-modal authentication system reaching an EER of 0.1% in an office environment and an EER of 0.5% in challenging noisy environments.