This thesis addresses the topic of electronic employee monitoring in non-unionized workplaces in Canada. Electronic employee monitoring is defined as including (1) the use of electronic devices to review and evaluate employees’ performance; (2) ‘electronic surveillance’; and (3) employers’ use of computer forensics. Detailed consideration is given to a variety of technologies, including computer, internet and e-mail monitoring, location awareness technologies (such as global positioning systems and radio frequency identification), as well as biometrics, and the developing case law surrounding these innovations. Analogies are drawn to the jurisprudence developing with respect to unionized workplaces and under statutory unjust dismissal regimes. This analysis leads to the conclusion that legislative reform is necessary, either through (1) the creation of parallel private sector privacy regimes, such as those in British Columbia and Alberta, mirroring existing federal legislation; (2) amendments to existing employment standards legislation; or (3) the enactment of a stand-alone surveillance statute. / October 2008

As the demand for higher and more sophisticated security solutions has dramatically increased, a trustworthy and a more intelligent authentication technology has to takeover. That is biometric authentication. Although biometrics provides promising solutions, it is still a pattern recognition and artificial intelligence grand challenge. More importantly, biometric data in itself are vulnerable and requires comprehensive protection that ensures their security at every stage of the authentication procedure including the processing stage. Without this protection biometric authentication cannot replace traditional authentication methods. This protection however cannot be accomplished using conventional cryptographic methods due to the nature of biometric data, its usage and inherited dynamical changes. The new protection method has to transform the biometric data into a secure domain where original information cannot be reversed or retrieved. This secure domain has also to be suitable for accurate authentication performance. In addition, due to the permanence characteristic of the biometric data and the limited number of valid biometrics for each individual, the transform has to be able to generate multiple versions of the same original biometric trait. This to facilitate the replacement and the cancellation of any compromised transformed template with a newer one without compromising the security of the system. Hence the name of the transform that is best known as cancellable biometric. Two cancellable face biometric transforms have been designed, implemented and analysed in this thesis, the Polynomial and Co-occurrence Mapping (PCoM) and the Randomised Radon Signatures (RRS). The PCoM transform is based on high-order polynomial function mappings and co-occurrence matrices derived from the face images. The secure template is formed by the Hadamard product of the generated metrics. A mathematical framework of the two-dimensional Principal Component Analysis (2DPCA) recognition is established for accuracy performance evaluation and analysis. The RRS transform is based on the Radon Transform (RT) and the random projection. The Radon Signature is generated from the parametric Radon domain of the face and mixed with the random projection of the original face image. The transform relies on the extracted signatures and the Johnson-Lindenstrauss lemma for high accuracy performance. The Fisher Discriminant Analysis (FDA) is used for evaluating the accuracy performance of the transformed templates. Each of the transforms has its own security analysis besides a comprehensive security analysis for both. This comprehensive analysis is based on a conventional measure for the Exhaustive Search Attack (ESA) and a new derived measure based on the lower-bound guessing entropy for Smart Statistical Attack (SSA). This entropy measure is shown to be greater than the Shannon lower-bound of the guessing entropy for the transformed templates. This shows that the transforms provide greater security while the ESA analysis demonstrates immunity against brute force attacks. In terms of authentication performance, both transforms have either maintained or improved the accuracy of authentication. The PCoM has maintained the recognition rates for the CMU Advance Multimedia Processing Lab (AMP) and the CMU Pose, Illumination & Expression (PIE) databases at 98.35% and 90.13% respectively while improving the rate for the Olivetti Research Ltd (ORL) database to 97%. The transform has achieved a maximum recognition performance improvement of 4%. Meanwhile, the RRS transform has obtained an outstanding performance by achieving zero error rates for the ORL and PIE databases while improving the rate for the AMP by 37.50%. In addition, the transform has significantly enhanced the genuine and impostor distributions separations by 263.73%, 24.94% and 256.83% for the ORL, AMP and PIE databases while the overlap of these distributions have been completely eliminated for the ORL and PIE databases.

Ο αυξανόμενος αριθμός χρήσης συστημάτων αναγνώρισης βιομετρικών δεδομένων κάνει επιτακτική την ανάγκη για ανάπτυξη λογισμικών με τα οποία θα επιτυγχάνεται τόσο η αξιοπιστία όσο και η ταχύτητα στην αναγνώριση των βιομετρικών δεδομένων ώστε ο χρήστης να έχει μεγαλύτερη ασφάλεια και να υφίσταται λιγότερη ταλαιπωρία. Σκοπός αυτή της ειδικής επιστημονικής εργασίας είναι η ανάπτυξη ενός λογισμικού για αναγνώριση δακτυλικών αποτυπωμάτων. Το συγκεκριμένο σύστημα χρησιμοποιεί για την αναγνώριση, τον δείκτη του δεξιού χεριού του χρήστη. Ουσιαστικά το δακτυλικό αποτύπωμα χρησιμοποιείται είτε για αναζήτηση της ταυτότητας του χρήστη από μια βάση δεδομένων με δακτυλικά αποτυπώματα είτε σαν κωδικός χρήστη για την πρόσβαση του χρήστη. Στην συγκεκριμένη εργασία αναπτύχθηκε ένα καινούργιο χαρακτηριστικό το οποίο βασίζεται στην κατεύθυνση της γραμμής (Line Directionality). Η εξαγωγή του χαρακτηριστικού αυτού είναι ιδιαίτερα γρήγορη με αποτέλεσμα ο χρόνος επεξεργασίας του συστήματος να μειώνεται αρκετά. Η ανάλυση του χαρακτηριστικού αυτού γίνεται στο τρίτο κεφάλαιο της εργασίας αυτής. Η εργασία αυτή αποτελείται από τέσσερα κεφάλαια. Στο πρώτο κεφάλαιο γίνεται εισαγωγή στα δακτυλικά αποτυπώματα και στα συστήματα αναγνώρισης βιομετρικών δεδομένων. Ακολούθως στο δεύτερο κεφάλαιο αναλύεται το σύστημα συλλογής δακτυλικών αποτυπωμάτων που αναπτύχθηκε προκειμένου να υλοποιηθεί η βάση με τα δακτυλικά αποτυπώματα, η οποία θα χρησιμοποιηθεί για την αξιολόγηση του συστήματος αναγνώρισης δακτυλικών αποτυπωμάτων. Στην συνέχεια στο τρίτο κεφάλαιο αναλύονται όλοι οι αλγόριθμοι του συστήματος αναγνώρισης δακτυλικών αποτυπωμάτων που αναπτύχθηκε σε αυτήν την εργασία . Το τέταρτο κεφάλαιο αποτελεί τον επίλογο της εργασίας αυτής όπου ερμηνεύονται τα αποτελέσματα της εφαρμογής του συστήματος στην βάση δακτυλικών αποτυπωμάτων και παρατίθενται τα κυριότερα συμπεράσματα που προέκυψαν. Επιπροσθέτως γίνονται προτάσεις για μελλοντική έρευνα. Οι κώδικες υλοποίησης του συστήματος αναγνώρισης δακτυλικών αποτυπωμάτων παρατίθενται στο παράρτημα της εργασίας αυτής. / Fingerprint acquisition, preprocessing and recognition consist one of the most important aspects in biometric recognition technologies. They are far and away one of the most widely used from all the various biometrics because they are non invasive and the acquisition uses low cost sensors. In this master thesis we developed a biometric recognition system based on fingerprints. An efficient fingerprint pre-processing and recognition algorithm was implemented in this biometric recognition system. A software application was developed using a Pc , the TMS320C6713 DSP starter kit module, along with the Authentec AFS 8600 fingerprint sensor, in order to create a fingerprint database for the evaluation of the fingerprint recognition system. Forty four people apart of the fingerprint database by enrolling 20 images of their fingerprints of the index finger of their right hand. Every fingerprint image has 96x96 pixels in a 250 ppi resolution, at 8-bit gray level. Biometric recognition system was developed by software in Matlab. The fingerprint acquisition software was developed in C and visual C++. First fingerprint images were subjective to a frequency and orientation processing. That was achieved using gabor filters that provided an image with best quality. Secondly, a method has been developed in order to extract the critical parameters of the fingerprint image which provides the features. Feature extraction base on line directionality. Core point of fingerprint image was detected in order to derive local information around core point. Finally classification algorithms were developed, which include training as well as evaluating phase. The type of classifier used was the Quadratic Bayesian, kNN , Probabilistic Neural Network and Single Hypothesis testing. A classification accuracy of 97,39% was achieved for the verification task and 93,2% for identification task.

Traditional appearance based face recognition (FR) systems use gray scale images, however recently attention has been drawn to the use of color images. Color inputs have a larger dimensionality, which increases the computational cost, and makes the small sample size (SSS) problem in supervised FR systems more challenging. It is therefore important to determine the scenarios in which usage of color information helps the FR system. In this thesis, it was found that inclusion of chromatic information in FR systems is shown to be particularly advantageous in poor illumination conditions. In supervised systems, a color input of optimal dimensionality would improve the FR performance under SSS conditions. A fusion of decisions from individual spectral planes also helps in the SSS scenario. Finally, chromatic information is integrated into a supervised ensemble learner to address pose and illumination variations. This framework significantly boosts FR performance under a range of learning scenarios.

This thesis addresses the topic of electronic employee monitoring in non-unionized workplaces in Canada. Electronic employee monitoring is defined as including (1) the use of electronic devices to review and evaluate employees’ performance; (2) ‘electronic surveillance’; and (3) employers’ use of computer forensics. Detailed consideration is given to a variety of technologies, including computer, internet and e-mail monitoring, location awareness technologies (such as global positioning systems and radio frequency identification), as well as biometrics, and the developing case law surrounding these innovations. Analogies are drawn to the jurisprudence developing with respect to unionized workplaces and under statutory unjust dismissal regimes. This analysis leads to the conclusion that legislative reform is necessary, either through (1) the creation of parallel private sector privacy regimes, such as those in British Columbia and Alberta, mirroring existing federal legislation; (2) amendments to existing employment standards legislation; or (3) the enactment of a stand-alone surveillance statute.

This thesis addresses the topic of electronic employee monitoring in non-unionized workplaces in Canada. Electronic employee monitoring is defined as including (1) the use of electronic devices to review and evaluate employees’ performance; (2) ‘electronic surveillance’; and (3) employers’ use of computer forensics. Detailed consideration is given to a variety of technologies, including computer, internet and e-mail monitoring, location awareness technologies (such as global positioning systems and radio frequency identification), as well as biometrics, and the developing case law surrounding these innovations. Analogies are drawn to the jurisprudence developing with respect to unionized workplaces and under statutory unjust dismissal regimes. This analysis leads to the conclusion that legislative reform is necessary, either through (1) the creation of parallel private sector privacy regimes, such as those in British Columbia and Alberta, mirroring existing federal legislation; (2) amendments to existing employment standards legislation; or (3) the enactment of a stand-alone surveillance statute.

Feature extraction is the most critical step in any human gait recognition system. Although gait is a dynamic process yet the static body parameters also play an important role in characterizing human gait. A few studies were performed in the past to assess the comparative relevance of static and dynamic gait features. There is, however, a lack of work in comparative performance analysis of dynamic gait features from different parts of the silhouettes in an appearance based setup. This dissertation presents a comparative study of dynamic features extracted from legs, arms and shoulders for gait recognition. Our study partially supports the general notion of leg motion being the most important determining factor in gait recognition. But it is also observed that features extracted from upper arm and shoulder area become more significant in some databases. The usefulness of the study hinges on the fact that lower parts of the leg are generally more noisy due to a variety of variations such as walking surface, occlusion and shadows. Dynamic features extracted from the upper part of the silhouettes posses significantly higher discriminatory power in such situations. In other situations these features can play a complementary role in the gait recognition process. We also propose two new feature extraction methods for gait recognition. The new methods use silhouette area signals which are easy and simple to extract. A significant performance increase is achieved by using the new features over the benchmark method and recognition results compare well to the other current techniques. The simplicity and compactness of the proposed gait features is their major advantage because it entails low computational overhead.

Feature extraction is the most critical step in any human gait recognition system. Although gait is a dynamic process yet the static body parameters also play an important role in characterizing human gait. A few studies were performed in the past to assess the comparative relevance of static and dynamic gait features. There is, however, a lack of work in comparative performance analysis of dynamic gait features from different parts of the silhouettes in an appearance based setup. This dissertation presents a comparative study of dynamic features extracted from legs, arms and shoulders for gait recognition. Our study partially supports the general notion of leg motion being the most important determining factor in gait recognition. But it is also observed that features extracted from upper arm and shoulder area become more significant in some databases. The usefulness of the study hinges on the fact that lower parts of the leg are generally more noisy due to a variety of variations such as walking surface, occlusion and shadows. Dynamic features extracted from the upper part of the silhouettes posses significantly higher discriminatory power in such situations. In other situations these features can play a complementary role in the gait recognition process. We also propose two new feature extraction methods for gait recognition. The new methods use silhouette area signals which are easy and simple to extract. A significant performance increase is achieved by using the new features over the benchmark method and recognition results compare well to the other current techniques. The simplicity and compactness of the proposed gait features is their major advantage because it entails low computational overhead.

Traditional appearance based face recognition (FR) systems use gray scale images, however recently attention has been drawn to the use of color images. Color inputs have a larger dimensionality, which increases the computational cost, and makes the small sample size (SSS) problem in supervised FR systems more challenging. It is therefore important to determine the scenarios in which usage of color information helps the FR system. In this thesis, it was found that inclusion of chromatic information in FR systems is shown to be particularly advantageous in poor illumination conditions. In supervised systems, a color input of optimal dimensionality would improve the FR performance under SSS conditions. A fusion of decisions from individual spectral planes also helps in the SSS scenario. Finally, chromatic information is integrated into a supervised ensemble learner to address pose and illumination variations. This framework significantly boosts FR performance under a range of learning scenarios.

Keystroke Dynamics enable biometric security systems by collecting and analyzing computer keyboard usage data. There are different approaches to classifying keystroke data and a method that has been gaining a lot of attention in the machine learning industry lately is the decision tree framework of XGBoost. XGBoost has won several Kaggle competitions in the last couple of years, but its capacity in the keystroke dynamics field has not yet been widely explored. Therefore, this thesis has attempted to classify the existing Beihang Keystroke Dynamics Database using XGBoost. To do this, keystroke features such as dwell time and flight time were extracted from the dataset, which contains 47 usernames and passwords. XGBoost was then applied to a binary classification problem, where the model attempts to distinguish keystroke feature sequences from genuine users from those of `impostors'. In this way, the ratio of inaccurately and accurately labeled password inputs can be analyzed. The result showed that, after tuning of the hyperparameters, the XGBoost yielded Equal Error Rates (EER) at best 0.31 percentage points better than the SVM used in the original study of the database at 11.52%, and a highest AUC of 0.9792. The scores achieved by this thesis are however significantly worse than a lot of others in the same field, but so were the results in the original study. The results varied greatly depending on user tested. These results suggests that XGBoost may be a useful tool, that should be tuned, but that a better dataset should be used to sufficiently benchmark the tool. Also, the quality of the model is greatly affected by variance among the users. For future research purposes, one should make sure that the database used is of good quality. To create a security system utilizing XGBoost, one should be careful of the setting and quality requirements when collecting training data