Masters Degrees (Applied Mathematics)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/96344

Browse

Browsing Masters Degrees (Applied Mathematics) by Subject "Biometric identification"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Ear-based biometric authentication
    (Stellenbosch : Stellenbosch University, 2019-04) Kohlakala, Aviwe; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT : In this thesis novel semi-automated and fully automated ear-based biometric authentication systems are proposed. Within the context of the semiautomated system, a region of interest (ROI) that contains the entire ear shell is manually speci ed by a human operator. However, in the case of the fully automated system the ROI is automatically detected using a suitable convolutional neural network (CNN), followed by morphological post-processing. The purpose of the CNN is to classify sub-images as either foreground (part of the ear shell) or background (homogeneous skin, jewellery, or hair). Independent of the ROI-detection procedure, each grey-scale input image, in its entirety, is subjected to Gaussian smoothing, followed by edge detection through an appropriate Canny- lter, and morphological edge dilation. The detected ROI serves as a mask for retaining only those edges associated with prominent contours of the ear shell. Features are subsequently extracted from each binary contour image using the discrete Radon transform (DRT). The aforementioned features are normalised in such a way that they are translation, rotation and scale invariant. A Euclidean distance measure is employed for the purpose of feature matching. Ear-based authentication is nally achieved by constructing a ranking veri er. Exhaustive experiments are conducted on two large international datasets. It is assumed that only one reference ear is available for each individual enrolled into the system. An experimental protocol is adopted that appropriately partitions the respective datasets based on ears that belong to training, validation, ranking and evaluation individuals. It is demonstrated that the pro ciency of the novel systems developed in this thesis compares favourably to those of existing systems.
  • Thumbnail Image
    Item
    Hand vein-based biometric authentication with limited training samples
    (Stellenbosch : Stellenbosch University, 2018-03) Beukes, Emile; Coetzer, Johannes; Swanepoel, J.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Applied Mathematics)
    ENGLISH ABSTRACT : A number of novel hand vein-based biometric authentication systems are proposed. Said systems are non-intrusive and may for example assist with user authentication at automated teller machines. An infrared image of either the dorsal or ventral surface of an individual's hand is acquired through specialised equipment, after which the geometrical properties of the hand are used to extract a suitable region of interest (ROI). A novel protocol, which is based on morphological reconstruction, is employed for the purpose of isolating the veins within the ROI. Feature vectors are extracted from the isolated veins through the calculation of the discrete Radon transform. The feature vectors are appropriately normalised in order to ensure rotational, translational and scale invariance. The dissimilarity between the corresponding feature vectors extracted from a questioned image and a reference image belonging to the claimed client are represented by an average Euclidean or dynamic time warping-based distance. A score-based or rank-based classi er is subsequently employed for authentication purposes. It is demonstrated that, when only one training sample (of arbitrary quality) is available per client, and the client is granted six opportunities for authentication, an average error rate (AER) of 2.85% is achievable for a data set that contains dorsal hand vein patterns from 100 individuals. In a scenario where the single training sample is guaranteed to be of very high quality and the client is granted only three opportunities for authentication, the AER may be reduced to 0.77%.
  • Thumbnail Image
    Item
    Modifying and generalising the Radon transform for improved curve-sensitive feature extraction
    (Stellenbosch : Stellenbosch University, 2017-12-01) Fick, Carlien; Coetzer, Johannes; Swanepoel, Jacques Philip; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT : In this thesis a novel and generic feature extraction protocol that is based on the well-known standard discrete Radon transform (SDRT) is presented. The SDRT is traditionally associated with computerised tomography and involves the calculation of projection profiles of an image from a finite set of angles. Although the SDRT has already been successfully employed for the purpose of feature extraction, it is limited to the detection of straight lines. The proposed feature extraction protocol is based on modifications to the SDRT that facilitate the detection of not only straight lines, but also curved lines (with various curvatures), as well as textural information. This is made possible by first constructing a novel appropriately normalised multiresolution polar transform (MPT) of the image in question. The origin of said MPT may be adjusted according to the type of features targeted. The SDRT, or the novel modified discrete Radon transform (MDRT) conceptualised in this thesis, is subsequently applied to the MPT. The extraction of textural information based on different textural periodicities is facilitated by considering different projection angles associated with the MDRT, while the extraction of textural information based on different textural orientations is facilitated by specifying different origins for the MPT. The extraction of information pertaining to curved lines is made possible by specifying origins for the MPT that are located at different distances from the edge of the image in question – the SDRT is subsequently applied to a given MPT from a specific angle of 90 . An existing system that only employs SDRT-based features constitutes a benchmark. Two novel texture-based systems, that target different textural periodicities and orientations respectively, are developed. A novel system, that constitutes a generalisation of the SDRT-based benchmark, and is geared towards the detection of different curved lines, is also developed. The proficiency of the proposed systems is gauged by considering a data set that contains authentic handwritten signature images and skilled forgeries associated with 51 writers. All of the proposed systems outperform the SDRTbased benchmark. The improvement in proficiency associated with each individual texture-based system is statistically significant. The proficiency of the proposed systems also compares favourably with that of existing state-of-theart systems within the context of offline signature verification.
  • Thumbnail Image
    Item
    Offline writer authentication
    (Stellenbosch : Stellenbosch University, 2021-12) Shumba, Sandura; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT: In this thesis a number of systems are proposed for the purpose of offline writer authentication. A text-dependent approach is adopted, since a very specific targeted handwritten word is considered for authentication purposes. Feature extraction is facilitated by calculating a number of projections of the targeted word from different angles. Two distinct categories of systems are proposed. The first category employs template matching and is based on the computation of the Euclidean distance and a dynamic time warping (DTW) distance between corresponding feature vectors, while the second category relies on machine learning techniques, that is support vector machines (SVMs) and quadratic discriminant analysis (QDA). Within the context of the proposed machine learning-based systems, a writer-independent protocol is followed. This is achieved by employing a DTW-based dichotomy transformation which converts a feature set in feature space into a dissimilarity vector-based representation in dissimilarity space. This dichotomy transformation is followed by writer-specific dissimilarity vector normalisation which significantly improves interclass separability. The DTW-based dichotomy transformation and writer-specific dissimilarity vector normalisation are novel within the context of offline writer authentication. The systems developed in this study are evaluated on a subset of the CEDAR-LETTER data set. It is demonstrated that the proficiency of the systems developed in this study are at least on par when compared to existing systems. The most proficient SVM-based system developed in this study achieves an AUC of 93% and an equal error rate (EER) of 14.93%.