H
Hazim Kemal Ekenel
Researcher at Istanbul Technical University
Publications - 231
Citations - 4571
Hazim Kemal Ekenel is an academic researcher from Istanbul Technical University. The author has contributed to research in topics: Facial recognition system & Convolutional neural network. The author has an hindex of 30, co-authored 215 publications receiving 3554 citations. Previous affiliations of Hazim Kemal Ekenel include Sabancı University & Boğaziçi University.
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Age and Gender Classification From Ear Images
TL;DR: It is indicated that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.
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Multimodal Age and Gender Classification Using Ear and Profile Face Images
TL;DR: This paper presents multimodal deep neural network frameworks for age and gender classification, which take input a profile face image as well as an ear image, to enhance the accuracy of soft biometric trait extraction from profile face images by additionally utilizing a promising biometric modality: ear appearance.
Proceedings ArticleDOI
G2-VER: Geometry Guided Model Ensemble for Video-based Facial Expression Recognition
Tanguy Albrici,Mandana Fasounaki,Saleh Bagher Salimi,Guillaume Vray,Behzad Bozorgtabar,Hazim Kemal Ekenel,Jean-Philippe Thiran +6 more
TL;DR: Improved face descriptors based on 2D CNNs and facial landmarks are proposed, and a modification to state-of-the-art expression recognition architectures to adapt them to video processing in a simple way.
Proceedings ArticleDOI
Cross-pose facial expression recognition
TL;DR: This paper uses Partial Least Squares to map the expressions from different poses into a common subspace, in which covariance between them is maximized, and shows that PLS can be effectively used for facial expression recognition across poses by training on coupled expressions of the same identity from two different poses.
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Apparent Age Estimation Using Ensemble of Deep Learning Models
TL;DR: In this paper, instead of using average age of the annotated face image as the class label, instead of grouping the face images that are within a specified age range, using these age groups and their age-shifted groupings, they have trained an ensemble of deep learning models.