Claus Vielhauer

Biometric hash based on statistical features of online signatures

Presents an approach to generating biometric hash values based on statistical features in online signature signals. Whilst the output of typical online signature verification systems are threshold-based true-false decisions, based on a comparison between test sample signals and sets of reference signals, our system responds to a signature input with a biometric hash vector, which is calculated based on an individual interval matrix. Especially for applications, which require key management strategies, hash values are of great interest, as keys can be derived directly from the hash value, whereas a verification decision can only grant or refuse access to a stored key. Further, our approach does not require storage of templates for reference signatures, thus increases the security of the system. In our prototype implementation, the generated biometric hash values are calculated on a pen-based PDA and used for key generation for a future secure data communication between a PDA and a server by encryption. First tests show that the system is actuality able to generate stable biometric hash values of the users and although the system was exposed to skilled forgeries, no test person was able to reproduce another subjects hash vector.

Communications and Multimedia Security

We propose to establish a standardised tool in fingerprint recognition robustness assessment, which is able to simulate a wide class of acquisition conditions, applicable to any given dataset and also of potential interest in forensic analysis. As an example, StirMark image manipulations (as being developed in the context of watermarking robustness assessment) are applied to fingerprint data to generate test data for robustness evaluations, thereby interpreting certain image manipulations as being highly related to realistic fingerprint acquisition conditions. Experimental results involving three types of fingerprint features and matching schemes (i.e. correlation-based, ridge feature-based, and minutiaebased) applied to FVC2004 data underline the need for standardised testing and a corresponding simulation toolset.

Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score.

Handwriting: feature correlation analysis for biometric hashes

In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation), the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches.

Using adapted Levenshtein distance for on-line signature authentication

A new method for on-line signature authentication is presented, which is based on a event-string modelling of features derived from pen-position and pressure signals of digitizer tablets. A distance measure well known from textual pattern recognition, the Levenshtein distance, is used for comparison of signatures and classification is carried out applying a nearest neighbor classifier. Results from a test set of 1376 signatures from 41 persons are presented, which have been conducted for four different feature sets. The results are rather encouraging, with correct identification rates of 96% at zero false classifications.

On non-invasive 2D and 3D Chromatic White Light image sensors for age determination of latent fingerprints

The feasibility of 2D-intensity and 3D-topography images from a non-invasive Chromatic White Light (CWL) sensor for the age determination of latent fingerprints is investigated. The proposed method might provide the means to solve the so far unresolved issue of determining a fingerprints age in forensics. Conducting numerous experiments for an indoor crime scene using selected surfaces, different influences on the aging of fingerprints are investigated and the resulting aging variability is determined in terms of inter-person, intra-person, inter-finger and intra-finger variation. Main influence factors are shown to be the sweat composition, temperature, humidity, wind, UV-radiation, surface type, contamination of the finger with water-containing substances, resolution and measured area size, whereas contact time, contact pressure and smearing of the print seem to be of minor importance. Such influences lead to a certain experimental variability in inter-person and intra-person variation, which is higher than the inter-finger and intra-finger variation. Comparing the aging behavior of 17 different features using 1490 time series with a total of 41,520 fingerprint images, the great potential of the CWL technique in combination with the binary pixel feature from prior work is shown. Performing three different experiments for the classification of fingerprints into the two time classes [0, 5 h] and [5, 24 h], a maximum classification performance of 79.29% (kappa=0.46) is achieved for a general case, which is further improved for special cases. The statistical significance of the two best-performing features (both binary pixel versions based on 2D-intensity images) is manually shown and a feature fusion is performed, highlighting the strong dependency of the features on each other. It is concluded that such method might be combined with additional capturing devices, such as microscopes or spectroscopes, to a very promising age estimation scheme.

Hologram watermarks for document authentications

We present a new integrity watermark based on hologram techniques for document authentication. We focus on ID-Card watermarks. The main goal is to illustrate the use of computer-generated hologram coding techniques, to produce content-related data to be embedded into the personal data printed on an identification card with watermarking techniques. We call this special application of an integrity watermark technique a hologram watermark.

Trainable sketch recognizer for graphical user interface design

In this paper we present a new algorithm for automatic recognition of hand drawn sketches based on the Levenshtein distance. The purpose for drawing sketches in our application is to create graphical user interfaces in a similar manner as the well established paper sketching. The new algorithm is trainable by every user and improves the recognition performance of the techniques which were used before for widget recognition. In addition, this algorithm ay serve for recognizing other types of sketches, such as letters, figures, and commands. In this way, there is no modality disruption at sketching time.

Approaches to biometric watermarks for owner authentification

One major application domain for digital watermarks is copyright protection. Besides the design of watermarking algorithms, technologies for copyright holder identification have to be investigated. To ensure authenticity of an individual person, a wide number of biometric procedures exist. We define and describe new biometric watermarks, which denote the application of biometric reference data of individuals within digital watermarks to identify and verify ownership. Amongst the two classes of physiological and senso-motoric biometric schemes, the later appears more appropriate for biometric watermarks, as only these provide implicit expressions of intention. As such, we choose on-line handwriting as an appropriate base technology for our three new scenarios in biometric watermarking. In the first approach, embedding keys are being generated from biometric reference data, which requires stable and robust features and leads to rather complex keys. To overcome the complexity boundaries, the second approach develops a biometric reference hash, allowing key look-ups in key certifying servers. Although this proceeding leads to less complex keys, it still requires stable features. The third approach describes the embedding of biometric reference data within a watermark, allowing owner verification by more variant features, but limitations apply due to capacity of watermarking systems and also protection of the reference data is required. While most handwriting-based verification systems are limited to signature contexts, we discuss two additional context types for user authentication: passphrases and sketches.

Towards fraud-proof ID documents using multiple data hiding technologies and biometrics

Identity documents, such as ID cards, passports, and drivers licenses, contain textual information, a portrait of the legitimate holder, and eventually some other biometric characteristics such as a fingerprint or handwritten signature. As prices for digital imaging technologies fall, making them more widely available, we have seen an exponential increase in the ease and the number of counterfeiters that can effectively forge documents. Today, with only limited knowledge of technology and a small amount of money, a counterfeiter can effortlessly replace a photo or modify identity information on a legitimate document to the extent that it is very diffcult to differentiate from the original. This paper proposes a virtually fraud-proof ID document based on a combination of three different data hiding technologies: digital watermarking, 2-D bar codes, and Copy Detection Pattern, plus additional biometric protection. As will be shown, that combination of data hiding technologies protects the document against any forgery, in principle without any requirement for other security features. To prevent a genuine document to be used by an illegitimate user,biometric information is also covertly stored in the ID document, to be used for identification at the detector.