Within-network ensemble for face attributes classification

Warning The system is temporarily closed to updates for reporting purpose.

Ahmed, Sara Atito and Yanıkoğlu, Berrin (2019) Within-network ensemble for face attributes classification. In: 20th International Conference on Image Analysis and Processing (ICIAP), Trento, Italy (Accepted/In Press)

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Face attributes classification is drawing attention as a research topic with applications in multiple domains, such as video surveillance and social media analysis. In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in an end-to-end framework considering the correlations between the different attributes. Furthermore, a novel ensemble learning technique is introduced within the network itself that reduces the time of training compared to ensemble of several models. Our approach outperforms the state-of-the-art of the attributes with an average improvement of almost 0.60% and 0.48% points, on the public CELEBA and LFWA datasets, respectively.

Item Type:Papers in Conference Proceedings
Subjects:Q Science > QA Mathematics > QA076 Computer software
ID Code:38700
Deposited By:Berrin Yanıkoğlu
Deposited On:04 Aug 2019 23:45
Last Modified:04 Aug 2019 23:45

Repository Staff Only: item control page