Embedding Local Quality Measures in Minutiae-Based Biometric Recognition

Degradation in data quality is still a main source of errors in the modern biometric recognition systems. However, the data quality can be embedded in the recognition methods at global and local levels to build more accurate biometric systems. Local quality measures represent the quality of local parts within a biometric sample. They are either combined into a global quality measure or directly embedded into the recognition techniques. Minutiae-based comparison is the main and the most common technique used for fingerprint recognition and high-resolution palmprint recognition in various security and forensic applications. The focus of this thesis is mainly on direct incorporation of the local quality measures into the state-of-the-art minutiae-based recognition methods, particularly those based on Minutiae Cylinder-Code (MCC). Firstly, we introduce cylinder quality measures as a new type of local quality measures associated with the local minutiae descriptors. Then, we propose several methods for incorporating such local quality measures into the biometric systems, in order to improve their recognition performance. Among them is a novel and efficient quality-based consolidation method for embedding minutiae quality and cylinder quality measures in MCC based comparison methods. We also propose a supervised embedding method based on a binary classification model, which requires labeled minutiae for training. Finally, we apply a variant of the proposed consolidation method for the challenging case of latent fingerprint and palmprint identification with embedded subjective and objective minutiae quality

Coverage Estimation in Floorplan Visual Sensor Networks

The issue of Coverage in visual sensor networks (VSNs) has attracted considerable attention due to sensors unique directional sensing characteristic. It answers the question that how well the target field is monitored by a network of sensors with video/image capturing capability. In floorplan scenario the network is to monitor a plane parallel to the sensors' deployment plane. Coverage probability estimation based on both the sensors and the network related parameters is a fundamental issue in this field. For a large scale application in which the sensors' deployment is done by dropping sensors from an airplane, random sensors' placement and orientation according to their respective distribution is a practical assumption. Although some studies exist on the coverage problem of floorplan VSNs, none of them has derived a closed form solution for the coverage estimation, which is the main contribution of this paper. The Simulation results validated the proposed mathematical solution