Face recognition from face signatures
This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected fa...
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2012
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ndltd-bl.uk-oai-ethos.bl.uk-5664102017-08-30T03:17:23ZFace recognition from face signaturesHanafi, MarsyitaPetrou, Maria2012This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected face is represented by a subimage. Then, an eye and mouth detection method is used to identify the coordinates of the eyes and mouth, which are then used to update the subimages so that the subimages contain only the face area. After that, a method based on Bayesian estimation and a fuzzy membership function is used to identify the actual faces on both subimages (obtained from the first and second steps). Then, a face similarity measure is used to locate the oval shape of a face in both subimages. The similarity measures between the two faces are compared and the one with the highest value is selected. In the recognition task, the Trace transform method is used to extract the face signatures from the oval shape face. These signatures are evaluated using the BANCA and FERET databases in authentication tasks. Here, the signatures with discriminating ability are selected and were used to construct a classifier. However, the classifier was shown to be a weak classifier. This problem is tackled by constructing a boosted assembly of classifiers developed by a Gentle Adaboost algorithm. The proposed methodologies are evaluated using a family album database.621.3Imperial College Londonhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566410http://hdl.handle.net/10044/1/10566Electronic Thesis or Dissertation |
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621.3 Hanafi, Marsyita Face recognition from face signatures |
description |
This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected face is represented by a subimage. Then, an eye and mouth detection method is used to identify the coordinates of the eyes and mouth, which are then used to update the subimages so that the subimages contain only the face area. After that, a method based on Bayesian estimation and a fuzzy membership function is used to identify the actual faces on both subimages (obtained from the first and second steps). Then, a face similarity measure is used to locate the oval shape of a face in both subimages. The similarity measures between the two faces are compared and the one with the highest value is selected. In the recognition task, the Trace transform method is used to extract the face signatures from the oval shape face. These signatures are evaluated using the BANCA and FERET databases in authentication tasks. Here, the signatures with discriminating ability are selected and were used to construct a classifier. However, the classifier was shown to be a weak classifier. This problem is tackled by constructing a boosted assembly of classifiers developed by a Gentle Adaboost algorithm. The proposed methodologies are evaluated using a family album database. |
author2 |
Petrou, Maria |
author_facet |
Petrou, Maria Hanafi, Marsyita |
author |
Hanafi, Marsyita |
author_sort |
Hanafi, Marsyita |
title |
Face recognition from face signatures |
title_short |
Face recognition from face signatures |
title_full |
Face recognition from face signatures |
title_fullStr |
Face recognition from face signatures |
title_full_unstemmed |
Face recognition from face signatures |
title_sort |
face recognition from face signatures |
publisher |
Imperial College London |
publishDate |
2012 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566410 |
work_keys_str_mv |
AT hanafimarsyita facerecognitionfromfacesignatures |
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1718521709445775360 |