Fulufhelo Vincent Nelwamondo

A fingerprint pattern classification approach based on the coordinate geometry of singularities

The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% is obtained on the five-class problem. This performance evaluation is done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002).

Two-Stage inter-cell layout design for cellular manufacturing by using ant colony optimization algorithms

Facility layout planning plays an important role in the manufacturing process and seriously impacts a companys profitability A well-planned layout can significantly reduce the total material handling cost The purpose of this paper is to develop a two-stage inter-cell layout optimization approach by using one of the popular meta-heuristics — the Ant Colony Optimization algorithm At the first stage, the cells are formed based on the part-machine clustering results obtained through the ant system algorithm In other words, we get the initial inter-cell layout after this stage The work at the second stage uses a hybrid ant system algorithm to improve the solution obtained at previous stage Different performance measures are also employed in this paper to evaluate the results.

Part-Machine Clustering: The Comparison between Adaptive Resonance Theory Neural Network and Ant Colony System

The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.

An Advanced Mutual-authentication Algorithm Using 3DES for Smart Card Systems

One of the most commonly used two-factor user authentication mechanisms nowadays is based on smart card and personal identity number or password. A scheme of this type is usually known as, a smart card based password authentication. The core feature of such a scheme is to enforce two-factor authentication in the sense that, the client must have the smart card and know the card PIN number in order to gain access to the server. In this paper, we propose and advanced remote mutual authentication scheme between a smart card, a smart card reader and the backend server database to ensure system security integrity. Our scheme provides high security and mutual authentication at a reasonable computational cost. Furthermore, it restricts most of the current attack mechanisms. It is simple and can be adapted to any kind of lightweight devices.

Application of GA-Fuzzy Controller design to Automatic Generation Control

The design of fuzzy logic controllers involves the rationalization of the Fuzzy Inferencing Rules and the appropriate selection of the input and output membership functions. This typically have been achieved by the application of expert knowledge of plant operation and by the appropriate selection of weighting gains. This paper presents the fuzzy logic controller with certain parameters which can be optimized to suite the specific application under control. Traditionally, this has been performed manually by design. However, contained within this study, Genetic Algorithms are applied as a plausible fuzzy logic controller optimizer (Genetic - Fuzzy Controller), and is applied to the Automatic Generation Control problem of large interconnected power systems.

Comparison of effective hough transform-based fingerprint alignment approaches

In this paper, two effective and mostly used Hough Transform (HT) based fingerprint alignment approaches are compared, namely; Local Match Based Alignment (LMBA) and Discretized Rotation Based Alignment (DRBA). The comparison was performed by considering different conditions of minutiae points, which are rotation, translation and the number of minutiae points. In addition, this research reports the advantages of understanding the quality and relationships between the wide varieties of existing HT based fingerprint alignment methods. Minutiae points extracted from fingerprints of FVC2000 database were used on the experiments to compare these approaches. The results revealed that LMBA approach performs better than the DRBA approach on minutiae points set with larger rotation and small number of points. The DRBA approach was found to perform better with minutiae points with large amount of translation, and the computational time was less than that of LMBA approach. However, the memory usage required in DRBA is greater than memory required in LMBA.

High resolution feature extraction from optical coherence tomography acquired internal fingerprint

Biometric fingerprint scanners scan the external skin features onto a 2D image. The performance of the automatic fingerprint identification system suffers if the finger skin is wet, worn out, fake fingerprint is used et cetera. Swept source optical coherence tomography (OCT) can be used to scan the internal skin features, up to the depth of the papillary layer. OCT is contactless and scans in three dimensions. The papillary contour represents an internal fingerprint, which does not suffer external skin problems. In this paper, we present a feature extraction method that extracts features at high resolution from the internal fingerprint. First curvature of an internal fingerprint cross-section is removed by fitting a third order polynomial and shifting each column in depth by the value of the fitted curve. A 2D image of the internal fingerprint is formed by concatenating the individual cross-sections, averaged across the papillary contour. The internal fingerprint image is then enhanced and features are extracted at high resolution. We have evaluated performance of feature extraction by matching extracted minutiae to those extracted manually. Matching accuracy shows that features can be extracted at high resolution from an OCT internal fingerprint.

Segmentation of Forensic Latent Fingerprint Images Lifted Contact-Less from Planar Surfaces with Optical Coherence Tomography

Lifting latent fingerprints through means that do not make contact with the surface where the fingerprint is imprinted, is advantageous in many ways. Some of these advantages include: being able to lift the print multiple times, there is no physical or chemical processing of a substrate required, the substrate can be concurrently analyzed for DNA for instance, and this can provide a non-destructive lifting of the fingerprint, something that can aid in scene preservation. In this paper, we present an automatic segmentation of latent fingerprint images lifted contact-less from planar surfaces using swept source optical coherence tomography. We do not perform any localization scans as we know the position of fingerprint impressions left on a substrate. The 3-D lifted scan is processed on a per cross-sectional image basis. First the cross-sections are filtered to reduce the effects of speckle noise, then the one dimensional Sobel edge detection is applied horizontally. The detected edge represents the substrate surface plus the latent fingerprint impression left on it. They are concatenated together to form a 2-D segmented image of the lifted fingerprint. After enhancement using contrast-limited adaptive histogram equalization, minutiae were extracted from the segmented images as an implicit quality evaluation procedure, on top of the subjective one carried out. Segmented images of latent fingerprints lifted off some substrates like glass and stainless steel were of sufficient quality for minutia extraction.

Novelty Detection-Based Internal Fingerprint Segmentation in Optical Coherence Tomography Images

Biometric fingerprint scanners scan the external skin features onto a 2-D image. The performance of the automatic fingerprint identification system suffers if the finger skin is wet, worn out, fake fingerprint is used et cetera. In this paper, we present an automatic segmentation of the papillary layer method, in 3-D swept source optical coherence tomography (SS-OCT) images. The papillary contour represents the internal fingerprint, which does not suffer external skin problems. The slices composing the 3-D image are filtered by the regularized Perona and Malik partial differential equations filter to minimize the effect of speckle noise. Then the corneum stratum is detected, which in turn leads to the extraction of the epidermis using prior knowledge of the epidermis depth. The epidermis is used as the target of the novelty detection that is applied to the image slices. The contour of the papillary layer is segmented as the boundary between the target and rejection classes resulting from novelty detection. The papillary contours are consistent with those segmented manually, with the modified Williams index above 0.9400 on average. The 3-D papillary contour represents an internal fingerprint.

Influence of the auditory canal number of segments and radius variation on the outer ear frequency response

A lumped transmission line model of the auditory canal is modified to study the influence of the number of segments used and the variation of the radius on the outer ear frequency response. A new second order polynomial is used to map the radius to the length along the narrow part of the auditory canal. The results found are compared to other literature results and expected theoretical results. The study shows that both an increase and decrease in the number of segments of the ear canal results in a distortion in the frequency response. The results obtained when using the radius-length mapping function show that the response found closely correlates with other literature results. The optimum representation of the frequency response was found when four number of segments and the radius-length mapping function were used. The use of a third order polynomial to further improve the relationship between the radius and the length of the ear canal is suggested for future research work.