|dc.contributor.author||Mohammad, Ahmad Saeed||
|dc.description||Title from PDF of title page viewed September 18, 2019||
|dc.description||Dissertation advisor: Reza Derakhshani||
|dc.description||Includes bibliographical references (pages 128-144)||
|dc.description||Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018||
|dc.description.abstract||The existence eyeglasses in human faces cause real challenges for ocular, facial,
and soft-based (such as eyebrows) biometric recognition due to glasses reﬂection, shadow,
and frame occlusion. In this regard, two operations (eyeglasses detection and eyeglasses
segmentation) have been proposed to mitigate the effect of occlusion using eyeglasses.
Eyeglasses detection is an important initial step towards eyeglass segmentation.
Three schemes of eye glasses detection have been proposed which are non-learning-based,
learning-based, and deep learning-based schemes. The non-learning scheme of eyeglasses
detection which consists of cascaded ﬁlters achieved an overall accuracy of 99.0% for VI
SOB and 97.9% for FERET datasets. The learning-based scheme of eyeglass detection
consisting of extracting Local Binary Pattern (LBP), Histogram of Gradients (HOG) and
fusing them together, then applying classiﬁers (such as Support Vector Machine (SVM),
Multi-Layer Perceptron (MLP), and Linear Discriminant Analysis (LDA)), and fusing the
output of these classiﬁers. The latter obtained a best overall accuracy of about 99.3% on
FERET and 100% on VISOB dataset. Besides, the deep learning-based scheme of eye
glasses detection showed a comparative study for eyeglasses frame detection using different Convolutional Neural Network (CNN) structures that are applied to Frame Bridge
region and extended ocular region. The best CNN model obtained an overall accuracy of
99.96% for ROI consisting of Frame Bridge.
Moreover, two schemes of eyeglasses segmentation have been introduced. The
ﬁrst segmentation scheme was cascaded convolutional Neural Network (CNN). This scheme
consists of cascaded CNN’s for eyeglasses detection, weight generation, and glasses segmentation, followed by mathematical and binarization operations. The scheme showed
a 100% eyeglasses detection and 91% segmentation accuracy by our proposed approach.
Also, the second segmentation scheme was the convolutional de-convolutional network.
This CNN model has been implemented with main convolutional layers, de-convolutional
layers, and one custom (lamda) layer. This scheme achieved better segmentation results
of 97% segmentation accuracy over the cascaded approach.
Furthermore, two soft biometric re-identiﬁcation schemes have been introduced
with eyeglasses mitigation. The ﬁrst scheme was eyebrows-based user authentication
consists of local, global, deep feature extraction with learning-based matching. The best
result of 0.63% EER using score level fusion of handcraft descriptors (HOG, and GIST)
with the deep VGG16 descriptor for eyebrow-based user authentication. The second
scheme was eyeglass-based user authentication which consisting of eyeglasses segmentation, morphological cleanup, features extraction, and learning-based matching. The best
result of 3.44% EER using score level fusion of handcraft descriptors (HOG, and GIST)
with the deep VGG16 descriptor for eyeglasses-based user authentication.
Also, an EER enhancement of 2.51% for indoor vs. outdoor (In: Out) light set
tings was achieved for eyebrow-based authentication after eyeglasses segmentation and
removal using Convolutional-Deconvolutional approach followed by in-painting.||eng
|dc.description.tableofcontents||Introduction -- Background in machine learning and computer vision -- Eyeglasses detection and segmentation -- User authentication using soft-biometric -- Conclusion and future work -- Appendix||
|dc.format.extent||xvii, 145 pages||
|dc.publisher||University of Missouri -- Kansas City||eng
|dc.subject.lcsh||Human face recognition (Computer science)||
|dc.subject.other||Dissertation -- University of Missouri--Kansas City -- Engineering||
|dc.subject.other||Dissertation -- University of Missouri--Kansas City -- Computer science||
|dc.title||Multi-Modal Ocular Recognition in presence of occlusion in Mobile Devices||eng
|thesis.degree.discipline||University of Missouri--Kansas City||
|thesis.degree.discipline||Electrical and Computer Engineering (UMKC)||
|thesis.degree.discipline||Telecommunications and Computer Networking (UMKC)||
|thesis.degree.name||Ph.D. (Doctor of Philosophy)||