We show that after applying exposure correction with the proposed model, the portrait matting quality increases significantly.
To train and evaluate the developed system, we collected and annotated images that represent face mask usage and face-hand interaction in the real world.
We have achieved very promising results, especially on the FERET dataset, generating visually appealing face images from ear image inputs.
Experimental results indicated that profile face images contain a rich source of information for age and gender classification.
no code implementations • 11 Mar 2019 • Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images.
We have first shown the importance of domain adaptation, when deep convolutional neural network models are used for ear recognition.