A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION

ABSTRACT Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that include

A Subspace Projection Methodology for Nonlinear Manifold Based Face Recognition

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations. Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective

Design and prototyping of a face recognition system on smart camera networks

The aim of this work is to design and develop a face recognition system running on smart camera networks. In many systems, these are passively used to send video to a recording server. The processing of the acquired data is mainly executed on remote and more powerful computers (or clusters of computers). In this thesis a distributed architecture was developed where computer vision algorithms are executed on smart cameras, which can exchange information to improve resource balance. A smart camera network has been defined specifying the roles that client nodes and a server have, and how nodes cooperate and communicate among them and with the server. Smart cameras, initially look for changes in the environment. When motion is detected, they perform face detection. Once a face is found, the camera itself processes it and tries to asses whom it belongs to, using a local cache of recognizers. This cache stores a portion of the whole information present on server side, and can be used to perform recognition tasks on the smart cameras. If a node is not able to identify a face it sends a query to the server. Finally, if the person’s id can be determined, either by the server or the client itself, the occurrence of the correspondent recognizer is notified to the nearest nodes. Human faces that were not recognized, are stored on the remote server and can be manually annotated. Clustering algorithms have been tested in order to automatically group faces belonging to unknown people on server side so they made the manual annotation easier. Extensive experiments have been performed on a freely available dataset to both assess the recognition performance and the benefits of using collaboration among cameras. Raspberry PI devices were used as camera network nodes. Various tests were performed in order to verify the efficiency of the face recognition approach on such devices