Alper Kanak

BioPSTM: a formal model for privacy, security, and trust in template-protecting biometric authentication

This paper presents a formal model, namely Biometric Privacy-Security-Trust Model BioPSTM, aiming to describe the tradeoff between privacy and security and their relationship with trust in biometric authentication systems. The relationship between trust and privacy-security pair requires a comprehensive approach that should consider user acceptance and the pricing between privacy and security. The proposed model is quite new in that it combines the formal formulation of tradeoff between privacy and security with trust over a users acceptance model. The formal model presents a three-dimensional approach to indicate demand responsive pricing between privacy, security, and trust. The model is interpreted over a general syndrome-based biometric template protection method by discussing possible privacy and security requirements. The proposed model has been applied on countries that are aware of biometric security technologies. The evaluation on country profiles presents an overall description of the user acceptance model and its relationship with biometric technologies. Copyright

New Turkish intelligibility test for assessing speech communication systems

This article describes a Turkish Intelligibility Test (TIT) in order to evaluate Quality of Service (QoS) of some speech communication systems (SCSs) based on Turkish phonetic properties. Since widely used speech communication systems are generally developed considering one language, the need of broadening linguistic coverage in designing next generation SCSs requires assessment methods in many broadly used languages including Turkish as well. In this article, selection of TIT material considering Turkish phonetic characteristics is discussed and the conduct of TIT including recording, preparing and presentation of test material is detailed. Experimental results, which present subjective intelligibility assessment outcomes of three well-known speech coders, are given in comparison with the results of the Diagnostic Rhyme Test (DRT) in North American English. Intelligibility assessment via TIT is expected to be a leading and critical concept in improving the intelligibility performance of Turkish processed by SCSs under varying acoustic environments.

An embedded fingerprint authentication system integrated with a hardware-based truly random number generator

Recent advances in information security requires randomly selected strong keys. Most of these keys are generated by software-based random number generators. However, implementing a Truly Random Number Generator (TRNG) without using a hardware-supported platform is not reliable. In this paper, a fingerprint authentication system using a hardware-based TRNG to produce a private key that encrypts the fingerprint template of a person is presented. The designed hardware can easily be mounted on a standard or embedded PC via its PCI interface to produce random number keys. Random numbers forming the private key is guaranteed to be true because it passes a two-level randomness test evaluated first on the FPGA then on the PC by applying the full NIST test suite. The whole system implements an AES-based encryption scheme to store the persons secret stored on a smart or glossary card safely. The main contribution of the work is the use of new-generation hardware-based TRNGs to enhance the security of a fingerprint authentication system.

A DCOM-based Turkish speech recognition system: TREN – turkish recognition ENgine

Turkish Recognition ENgine (TREN) is a modular, Hidden Markov Model based (HMM-based), speaker independent and Distributed Component Object Model based (DCOM-based) speech recognition system. TREN contains specialized modules that allow a fully interoperable platform including a Turkish speech recognizer, a feature extractor, an end-point detector and a performance monitoring module. TREN deals with the interaction between two layers constituting the distributed architecture of TREN. The first layer is the central server, which applies some speech signal preprocessing and distributes the recognition calls to the appropriate remote servers according to their current CPU load of the recognition process. The second layer is composed of the remote servers performing the critical recognition task. In order to increase the recognition performance, a Turkish telephony speech database with a very large word corpus is collected and statistically the widest span of triphones representing Turkish is examined. TREN has been used to assist speech technologies which require a modular and multithreaded recognizer with dynamic load sharing facilities.

Tracking music from media using reduced pitch class profile features

In this paper, a system that robustly searches and matches a music input signal to a music collection database using a hash table that is constructed from n-grams of reduced tonal profile. Since the problem that is being studied requires high performance, efficiency and scalability, not only the retrieval accuracy should be high, but also the systems workload on the processing unit and the memory should be in acceptable ranges. With respect to other conventional features, the tonal profile features extracted in this work requires much less space. From the tonal features n-gram blocks are constructed and used in a look up table. Whenever the input signal satisfies some constraints, matching and retrieval are performed The results show that the computation performance and the retrieval accuracy is at promising levels.

TREN- Turkish speech recognition platform

TREN (Turkish Recognition ENgine) is a modular, HMM-based (Hidden Markov Model) and speaker-independent speech recognition system whose system software architecture is based on Distributed Component Object Model (DCOM). TREN contains specialized modules that allow a full interoperable platform including a Turkish speech recognizer, feature extractor, end-point detector and a performance monitoring module. TREN has basically two layers: First layer is the central server that distributes the recognition calls to the appropriate remote servers according to their current CPU load of the recognition process after some speech signal preprocessing and the second layer consists of the remote servers which performs the critical recognition task. This component-based architecture enables TREN applicable to distributed environments. TREN is also trained by considering a wide variety of very common words those best represent the Turkish language. In order to obtain a such database a very large word corpus is collected and statistically the widest span of triphones representing Turkish is examined. TREN has been used to assist speech technologies which require a modular and multithreaded recognizer with dynamic load sharing facilities.

Improving a cost-effective fingerprint verification system by using cascade fusion

When used with a good fingerprint enhancement algorithm, cost-effective and less complex features may perform well for recognition of low-quality fingerprints. Especially, improving systems using such features is a promising point in large-scale fingerprint verification systems. In this study, scores obtained from features which are extracted from the wedge and ring based tessellation of a fingerprint frequency image are fused with the scores of phase-only correlation based matching method. The matching scores obtained from two independent information sources are improved by applying the proposed cascade score-level fusion scheme. In this scheme, first the reliability of the wedge-ring verification scores are evaluated; if the reliability of the wedge-ring features are not sufficient the phase-only correlation scores are used to make a final decision. In order to improve the verification system a Short-Time Fourier Transform (STFT) based enhancement algorithm and complex filtering to detect reference point on a fingerprint image is applied.

A hardware-implemented truly random key generator for secure biometric authentication systems

Recent advances in information security requires strong keys which are randomly generated. Most of the keys are generated by the softwares which use software-based random number generators. However, implementing a True Random Number Generator (TRNG) without using a hardware-supported platform is not reliable. In this paper, a biometric authentication system using a FPGA-based TRNG to produce a private key that encrypts the face template of a person is presented. The designed hardware can easily be mounted on standard or embedded PC via its PCI interface to produce random number keys. Random numbers forming the private key is guaranteed to be true because it passes a two-level randomness test. The randomness test is evaluated first on the hardware then on the PC by applying the full NIST test suite. The whole system implements an AES-based encryption scheme to store the persons secret safely. Assigning a private key which is generated by our TRNG guarantees a unique and truly random password. The system stores the Wavelet Fourier-Mellin Transform (WFMT) based face features in a database with an index number that might be stored on a smart or glossary card. The objective of this study is to present a practical application integrating any biometric technology with a hardware-implemented TRNG.

A discussion on fusion/confusion at score level in unimodal biometric systems

Fusing biometric information at different levels is one of the widely-preferred techniques for better accuracy. Researchers attempt to decrease error rates by fusing either multiple biometrics or apply sensor-, feature-, score- or decision-level fusion in unimodal biometric systems. Although it increases the complexity of systems, fusion works usually well. However, in some cases score-level fusion may degrade the performance instead of improving the system. In this study a discussion on fusion whether it causes confusion or not is raised by presenting a unimodal face fusion approach. Results show that if fusion is not designed properly, it may cause confusion.

BioTAM: a technology acceptance model for biometric authentication systems

The increasing demand on biometric authentication systems (BASs) has brought the need of secure and privacy-preserving solutions accepted by a wider community of users. The decision makers pay a great attention to how people react to BASs and their opinions about the features and procedures of the system. In this work, a generic Biometric Technology Acceptance Model (BioTAM) is proposed. BioTAM encounters trust as an objective measure of privacy-security tradeoff, public willingness and user confidence. BioTAM takes into account social and human factors which prominently affect the wider dissemination and easy penetration of BASs. To scrutinise peoples behavioural intention to use a BAS, BioTAM melds traditional Technology Acceptance Model constructs and the trust model offered as a new construct. In order to inspire stakeholders on how BioTAM can be used to assess a BAS, a sample case study is investigated.