SUSIG: An On-line Handwritten Signature Database, Associated Protocols and Benchmark Results”

In this paper we describe a new online signature database which is available for use in developing or testing signature verification systems. The SUSIG database consists of two parts, collected using different pressure sensitive tablets (one with and one without LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3000 genuine and 2000 skilled forgery signatures. One of the greatest problems in constructing such a database is obtaining skilled forgeries: people who donate to a database do not have the same motivation, nor the acquired skill of a true forger intent on passing as the claimed identity. In this database, skilled forgeries were collected such that forgers saw the actual signing process played-back on the monitor and had a chance of practicing. Furthermore, for a subset of the skilled forgeries (highly skilled forgeries), the animation was mapped onto the LCD screen of the tablet so that the forgers could watch, as well as trace over the signature. Forgers in this group were also informed of how close they were to the reference signatures, so that they could improve the forgery and forgeries that were visibly dissimilar were not submitted. We describe the signature acquisition process, approaches used to collect skilled forgeries, and verification protocols which should be followed while assessing performance results. We also report performance of a state of the art online signature verification algorithm using the SUSIG database and the associated protocols. The results of this system show that the highly skilled forgery set composed of traced signatures is more difficult compared to the skilled forgery set. Furthermore, single session protocols are easier than across-session protocols. The database is made available for academic purposes through http://biometrics.sabanciuniv.edu/SUSIG

Minimum energy configurations of the 2-dimensional HP-model of proteins by self-organizing networks

We use self-organizing maps (SOM) as an efficient tool to find the minimum energy configurations of the 2-dimensional HP-models of proteins. The usage of the SOM for the protein folding problem is similar to that for the Traveling Salesman Problem. The lattice nodes represent the cities whereas the neurons in the network represent the amino acids moving towards the closest cities, subject to the HH interactions. The valid path that maximizes the HH contacts corresponds to the minimum energy configuration of the protein. We report promising results for the cases when the protein completely fills a lattice and discuss the current problems and possible extensions. In all the test sequences up to 36 amino acids, the algorithm was able to find the global minimum and its degeneracies

Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

Biometric cryptosystem using online signatures

Biometric cryptosystems combine cryptography and biometrics to benefit from the strengths of both fields. In such systems, while cryptography provides high and adjustable security levels, biometrics brings in non-repudiation and eliminates the need to remember passwords or to carry tokens etc. In this work we present a biometric cryptosystems which uses online signatures, based on the Fuzzy Vault scheme of Jules et al. The Fuzzy Vault scheme releases a previously stored key when the biometric data presented for verification matches the previously stored template hidden in a vault. The online signature of a person is a behavioral biometric which is widely accepted as the formal way of approving documents, bank transactions, etc. As such, biometric-based key release using online signatures may have many application areas. We extract minutiae points (trajectory crossings, endings and points of high curvature) from online signatures and use those during the locking & unlocking phases of the vault. We present our preliminary results and demonstrate that high security level (128 bit encryption key length) can be achieved using online signatures

Combining multiple biometrics to protect privacy

As biometrics are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. The association of fingerprints with criminals raises further concerns. On the other hand, the alternative suggestion of keeping biometric data in smart cards does not solve the problem, since forgers can always claim that their card is broken to avoid biometric verification altogether. We propose a biometric authentication framework which uses two separate biometric features combined to obtain a non-unique identifier of the individual, in order to address privacy concerns. As a particular example, we demonstrate a fingerprint verification system that uses two separate fingerprints of the same individual. A combined biometric ID composed of two fingerprints is stored in the central database and imprints from both fingers are required in the verification process, lowering the risk of misuse and privacy loss. We show that the system is successful in verifying a person’s identity given both fingerprints, while searching the combined fingerprint database using a single fingerprint, is impractical

Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

An online handwriting recognition system for Turkish

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered