Sung-Hyuk Cha

Individuality of handwriting.

Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.

On measuring the distance between histograms

Abstract A distance measure between two histograms has applications in feature selection, image indexing and retrieval, pattern classification and clustering, etc. We propose a distance between sets of measurement values as a measure of dissimilarity of two histograms. The proposed measure has the advantage over the traditional distance measures regarding the overlap between two distributions; it takes the similarity of the non-overlapping parts into account as well as that of overlapping parts. We consider three versions of the univariate histogram, corresponding to whether the type of measurement is nominal, ordinal, and modulo and their computational time complexities are Θ(b), Θ(b) and O(b2) for each type of measurements, respectively, where b is the number of levels in histograms.

Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions

A long-text-input keystroke biometric system was developed for applications such as identifying perpetrators of inappropriate e-mail or fraudulent Internet activity. A Java applet collected raw keystroke data over the Internet, appropriate long-text-input features were extracted, and a pattern classifier made identification decisions. Experiments were conducted on a total of 118 subjects using two input modes - copy and free-text input - and two keyboard types - desktop and laptop keyboards. Results indicate that the keystroke biometric can accurately identify an individual who sends inappropriate email (free text) if sufficient enrollment samples are available and if the same type of keyboard is used to produce the enrollment and questioned samples. For laptop keyboards we obtained 99.5% accuracy on 36 users, which decreased to 97.9% on a larger population of 47 users. For desktop keyboards we obtained 98.3% accuracy on 36 users, which decreased to 93.3% on a larger population of 93 users. Accuracy decreases significantly when subjects used different keyboard types or different input modes for enrollment and testing.

A Genetic Algorithm for Constructing Compact Binary Decision Trees

Tree-based classifiers are important in pattern recognitio n and have been well studied. Although the problem of finding an optimal decision tree has r eceived attention, it is a hard optimization problem. Here we propose utilizing a genetic algorithm to improve on the finding of compact, near-optimal decision trees. We present a method to encode and decode a decision tree to and from a chromosome where genetic operators such as mutation and crossover can be applied. Theoretical properties of decisi on trees, encoded chromosomes, and fitness functions are presented.

Establishing handwriting individuality using pattern recognition techniques

We undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of one thousand five hundred individuals, representative of the US population with respect to gender age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the expert document examiner.

Individuality of handwriting: a validation study

Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of 1500 individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the expert document examiner.

Enhancing Binary Feature Vector Similarity Measures

Similarity and dissimilarity measures play an important role in pattern classification and clustering. For a century, researchers have searched for a good measure. Here, we review, categorize, and evaluate various binary vector similarity / dissimilarity measures. One of the most contentious disputes in the similarity measure selection problem is whether the measure includes or excludes negative matches. While inner-product based similarity measures consider only positive matches, other conventional measures credit both positive and negative matches equally. Hence, we propose an enhanced similarity measure that gives variable credits and show that it is superior to conventional measures in IRIS biometric authentication and offline handwritten character recognition applications. Finally, the proposed similarity measure can be further boosted by applying weights and we demonstrate that it outperforms the weighted Hamming distance.

An investigation of keystroke and stylometry traits for authenticating online test takers

The 2008 federal Higher Education Opportunity Act requires institutions of higher learning to make greater access control efforts for the purposes of assuring that students of record are those actually accessing the systems and taking exams in online courses by adopting identification technologies as they become more ubiquitous. To meet these needs, keystroke and stylometry biometrics were investigated towards developing a robust system to authenticate (verify) online test takers. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 40 test-taking students enrolled in a university course. The best equal-error-rate performance on the keystroke system was 0.5% which is an improvement over earlier reported results on this system. The performance of the stylometry system, however, was rather poor and did not boost the performance of the keystroke system, indicating that stylometry is not suitable for text lengths of short-answer tests unless the features can be substantially improved, at least for the method employed.

On the individuality of the iris biometric

Biometric authentication has been considered a model for quantitatively establishing the discriminative power of biometric data. The dichotomy model classifies two biometric samples as coming either from the same person or from two different people. This paper reviews features, distance measures, and classifiers used in iris authentication. For feature extraction we compare simple binary and multi-level 2D wavelet features. For distance measures we examine scalar distances such as Hamming and Euclidean, feature vector and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the histogram distance, and a support vector machine classifier.

Behavioral biometric verification of student identity in online course assessment and authentication of authors in literary works

Keystroke and stylometry behavioral biometrics were investigated with the objective of developing a robust system to authenticate students taking online examinations. This work responds to the 2008 U.S. Higher Education Opportunity Act that requires institutions of higher learning undertake greater access control efforts, by adopting identification technologies as they become available, to assure that students of record are those actually accessing the systems and taking the exams in online courses. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 30 students taking examinations in a university course. The performance of the keystroke system was 99.96% and 100.00%, while that of the stylometry system was considerably weaker at 74% and 78%, on test input of 500 and 1000 words, respectively. To further investigate the stylometry system, a separate study on 30 book authors achieved performance of 88.2% and 91.5% on samples of 5000 and 10000 words, respectively, and the varied performance over the population of authors was analyzed.