A Bimodal Biometric Student Attendance System

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometric attendance systems are unimodal. Unimodal biometric systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students’ attendance. The students’ faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision