Thesis (Ph. D.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xv, 136 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 125-136).

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 88-90).

Thesis (M.S.)--Youngstown State University, 2008. / Includes bibliographical references (leaves 17-19). Also available via the World Wide Web in PDF format.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains vii, 57 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 53-57).

Thesis (Ph. D.)--Lehigh University, 2006. / Includes vita. Includes bibliographical references (leaves 175-189).

No description available.

Magister Scientiae - MSc / This thesis addresses analysis, evaluation and simulation of low complexity face detection algorithms and tracking that could be used on mobile phones. Network access control using face recognition increases the user-friendliness in human-computer interaction. In order to realize a real time system implemented on handheld devices with low computing power, low complexity algorithms for face detection and face tracking are implemented. Skin color detection algorithms and face matching have low implementation complexity suitable for authentication of cellular network services. Novel approaches for reducing the complexities of these algorithms and fast implementation are introduced in this thesis. This includes a fast algorithm for face detection in video sequences, using a skin color model in the HSV (Hue-Saturation-Value) color space. It is combined with a Gaussian model of the H and S statistics and adaptive thresholds. These algorithms permit segmentation and detection of multiple faces in thumbnail images. Furthermore we evaluate and compare our results with those of a method implemented in the Chromatic Color space (YCbCr). We also test our test data on face detection method using Convolutional Neural Network architecture to study the suitability of using other approaches besides skin color as the basic feature for face detection. Finally, face tracking is done in 2D color video streams using HSV as the histogram color space. The program is used to compute 3D trajectories for a remote surveillance system. / South Africa

No description available.

Component-based automatic face recognition has been of interest to a growing number of researchers in the past fifteen years. However, the main challenge remains the automatic extraction of facial components for recognition in different face orientations without any human intervention; or any assumption on the location of these components. In this work, we investigate a solution to this problem. Facial components: eyes, nose, and mouth are firstly detected in different orientations of face. To ensure that the components detected are appropriate for recognition, the Support Vector Machine (SVM) classifer is applied to identify facial components that have been accurately detected. Thereafter, features are extracted from the correctly detected components by Gabor Filters and Zernike Moments combined. Gabor Filters are used to extract the texture characteristics of the eyes and Zernike Moments are applied to compute the shape characteristics of the nose and the mouth. The texture and the shape features are concatenated and normalized to build the final feature vector of the input face image. Experiments show that our feature extraction strategy is robust, it also provides a more compact representation of face images and achieves an average recognition rate of 95% in different face orientations. / Thesis (M.Sc.)-University of KwaZulu-Natal, 2008.