Facial Attribute Recognition and its Application in Drug Abuse Detection

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dc.contributor.advisorYanushkevich, Svetlana
dc.contributor.authorTekkam Gnanasekar, Sudarsini
dc.contributor.committeememberCuriel, Laura
dc.contributor.committeememberSafavi-Naini, Rei
dc.date2019-11
dc.date.accessioned2019-07-05T18:13:52Z
dc.date.available2019-07-05T18:13:52Z
dc.date.issued2019-07-04
dc.description.abstractFace attribute analysis is a valuable aide in biometric-based human identification. This is a challenging task due to variations in lighting, occlusion, pose and other variables. This work proposes an effective and robust approach to detect up to 40 face attributes using deep machine learning models such as Convolutional Neural Networks(CNNs). The focus is on using different pretrained CNNs to extract features from the intermediate CNN layers for face attribute recognition. Also, feature level fusion is proposed by concatenating the features extracted from the intermediate layers of the CNNs, thus using ensemble features. Classification of face attributes was performed using a linear Support Vector Machine (SVM) and end-to-end training of using CNN for both feature extraction and classification was also considered. The proposed face attribute recognition is also applied in this study for the purpose of detection of the selected attributes that are indicative of the prolonged illicit drug abuse, using the public database FacesOfMeth. Both the deep neural networks for feature extraction and attribute detection, and machine reasoning performed using Bayesian networks are applied. The feasibility and performance of the proposed approach on the public databases of faces with labeled attributes is evaluated in terms of accuracy, precision, sensitivity and specificity.en_US
dc.identifier.citationTekkam Gnanasekar, S. (2019). Facial Attribute Recognition and its Application in Drug Abuse Detection (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36708
dc.identifier.urihttp://hdl.handle.net/1880/110588
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subject.classificationEducation--Technologyen_US
dc.subject.classificationStatisticsen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.titleFacial Attribute Recognition and its Application in Drug Abuse Detectionen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
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