Elastic Bunch Graph Matching-Based Models To Recognize Faces Exposed To Occlusion, Expression And Illumination

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dc.contributor.authorLahasan, Badr Mohammed Omar
dc.date.accessioned2018-02-08T01:00:43Z
dc.date.available2018-02-08T01:00:43Z
dc.date.issued2017-06
dc.description.abstractAutomated face recognition is still a challenging task where the performance of a typical face recognition algorithm usually degrades considerably due to several variations such as facial occlusions, expressions, illumination or a combination of these conditions and the so-called Single Sample Per Subject (SSPS) problem. This study attempts to address the aforementioned challenges by improving the effectiveness of graph-based models that underlay face recognition. First, a new mechanism for facial landmarks formulation and selection is proposed. Thereafter, two automated partial facegraphs models are presented. The first model is the Harmony Search Oriented- EBGM (HSO-EBGM), a reformulated conventional EBGM algorithm, was introduced to recognize occluded faces. This is done by deploying a component level sub-graph mechanism and selecting the facial landmark automatically using harmony search algorithm. The second model is proposed to resolve the problem of face recognition associated with SSPS. To accomplish this goal, an automated graph-based approach was devised and referred to as Optimized Symmetric Partial Facegraphs (OSPF). The OSPF approach entailed the development of memetic based framework and exploitation of the bilateral symmetry property of human faces to recognize faces using SSPS. The proposed models were evaluated on four widely used facial corpora with partial occlusions, facial expression and illumination variation. The proposed HSO-BGM model yielded recognition accuracies better than the state-of-the-art approaches for facial expression and illumination while it is similar for occlusion where an average recognition rate of 89.36% is achieved on the AR face dataset. As for the proposed OSPF model, results indicated that this model was significantly more successful in discriminating faces in the aforementioned conditions than the state-of-art approaches when evaluated on the same facial corpora where an average recognition rates of 98.3%, 98.2%, 97.9% and 98.22% were obtained for face images choose from AR, FRGC, GBU and the frontal LFW face datasets respectively. The results corroborated the ability of the proposed models to handle most of the face recognition challenges such as partial occlusions, different expressions, varying illumination and SSPS problem.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5493
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectThe aforementioned challenges by improving the effectiveness ofen_US
dc.subjectgraph-based models that underlay face recognitionen_US
dc.titleElastic Bunch Graph Matching-Based Models To Recognize Faces Exposed To Occlusion, Expression And Illuminationen_US
dc.typeThesisen_US
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