Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/50725
Title: Face recognition based on multi-scale local features
Authors: Geng, Cong
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2012
Source: Geng, C. (2012). Face recognition based on multi-scale local features. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The ability to recognize human faces is a demonstration of incredible human intelligence. Over the last four decades, attempts from diverse areas are made to replicate this outstanding visual perception of human beings in machine recognition of faces. Within the face recognition literature, researchers have centered the debate on how human beings perceive human faces and this has become an important and active research area. Psychologists concluded that holistic and local feature based approaches are dual routes to the face recognition. Although holistic based approaches have attained certain level of maturity, in general they require a preprocessing procedure to normalize the face image variations in pose, scale and illumination. This is not an easy task because it depends on the accurate detection of at least two landmarks from the face image. As a result, most approaches work on the normalized face images based on the manually identified landmarks. The recognition performance deteriorates considerably if the manual process is replaced by an automatic landmark detection algorithm. Moreover, global features are sensitive to image variations in scale, facial expression, pose and occlusion. Most of the holistic approaches are dependent on the training databases because knowledge about the face discrimination is generalized by machine learning from the face samples. A representative training database is necessary, which, however, is not available in many applications.
URI: https://hdl.handle.net/10356/50725
DOI: 10.32657/10356/50725
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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