Face recognition using Support Vector Machine and multiscale directional image representation methods: A comparative study

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dc.contributor.authorDa Costa D.M.M.
dc.contributor.authorPeres S.M.
dc.contributor.authorLima C.A.M.
dc.contributor.authorMustaro P.
dc.date.accessioned2024-03-13T00:56:20Z
dc.date.available2024-03-13T00:56:20Z
dc.date.issued2015
dc.description.abstract© 2015 IEEE.In recent years, human identification based on face recognition has attracted the attention of the scientific community and the general public due to its wide range of applications. A face recognition system involves three important phases: face detection, feature extraction and classification (identification and/or verification). The robustness of face recognition could be improved by treating the variations in these stages. One of the main issues in design of face recognition system is how to extract discriminative facial features. A precise extraction of a representative feature set will improve the performance of a face recognition system. Various techniques have been used to represent images efficiently, of which the most well-known and widely applied are Wavelet, Contourlet, Shearlet and Curvelet Transform. Their ability to capture localized time-frequency information of image motivates their use for feature extraction. In this paper, we conduct a systematic empirical study on these transforms as feature extractors from face images. To further reduce the feature dimensionality, we adopt Principal Component Analysis and Linear Discriminant Analysis to select the most discriminative feature sets. The performance levels delivered by each transform are contrasted in terms of the accuracy measure computed over the outputs generated by the Support Vector Machine classifier (SVM). Experimental results conducted on a publicly available database are reported whereby we observe that the Curvelet Transform followed by the Wavelet Transform significantly outperform the others according to accuracy measure calculated over the SVM classifier.
dc.description.volume2015-September
dc.identifier.doi10.1109/IJCNN.2015.7280699
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36149
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rightsAcesso Restrito
dc.subject.otherlanguageContourlet Transform
dc.subject.otherlanguageCurvelet Transform
dc.subject.otherlanguageFace Recognition
dc.subject.otherlanguageShearlet Transform
dc.subject.otherlanguageSupport Vector Machine
dc.subject.otherlanguageWavelet Transform
dc.titleFace recognition using Support Vector Machine and multiscale directional image representation methods: A comparative study
dc.typeArtigo de evento
local.scopus.citations13
local.scopus.eid2-s2.0-84950970469
local.scopus.subjectContourlet transform
local.scopus.subjectCurvelet transforms
local.scopus.subjectFace recognition systems
local.scopus.subjectFeature extraction and classification
local.scopus.subjectLinear discriminant analysis
local.scopus.subjectShearlet transforms
local.scopus.subjectSupport vector machine classifiers
local.scopus.subjectTime frequency information
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84950970469&origin=inward
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