Ear-based biometric authentication

Kohlakala, Aviwe (2019-04)

Thesis (MSc)--Stellenbosch University, 2019.

Thesis

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.

AFRIKAANSE OPSOMMING : In hierdie tesis word nuwe semi- en vol-outomatiese oor-gebaseerde biometriese verifieëringstelsels voorgestel. Binne die konteks van die semi-automatiese stelsel word 'n fokusgebied (FG), wat die hele oorskulp bevat, deur 'n menslike operateur gespesi seer. In die geval van die vol-outomatiese stelsel word bogenoemde FG egter outomaties deur 'n geskikte konvolusie-neuraalnetwerk (KNN) gevind, gevolg deur morfologiese na-verwerking. Die doel van die KNN is om sub-beelde as óf voorgrond (deel van die oorskulp) óf agtergrond (homogene vel, juweliersware, óf hare) te klassi seer. Onafhanklik van die FG-herkenningsprosedure, word elke grysskaal-invoerbeeld in geheel aan Guassiese vergladding onderwerp, gevolg deur randherkenning met behulp van 'n geskikte Canny- lter, en morfologiese randverdikking. Die herkende FG dien as 'n masker wat slegs daardie randte wat met prominente kontoere van die oorskulp geassosieer word, behou. Kenmerke word vervolgens vanuit elke binêre kontoerbeeld met behulp van die diskrete Radon transform onttrek. Bogenoemde kenmerke word sodanig genormaliseer dat dit translasie-, rotasie- en skaal-invariant is. 'n Euklidiese afstandsmaat word vir die doel van kenmerkpassing aangewend. Oor-gebaseerde herkenning word laastens bewerkstellig deur van 'n rangorde-veri eerder gebruik te maak. Uitgebreide eksperimente word op twee groot internasionale datastelle uitgevoer. Daar word aanvaar dat slegs een verwysingsoor vir elke geregistreerde individu beskikbaar is. 'n Eksperimentele protokol wat die onderskeie datastelle sinvol op grond van afrigtings-, bekragtigings-, ordenings- en evalueringsindividue verdeel, word gevolg. Daar word aangetoon dat die vaardigheid van die nuwe stelsels wat in hierdie tesis ontwikkel is, goed met dié van bestaande stelsels vergelyk.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/105976
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