作者
Gokhan Ozbulak, Yusuf Aytar, Hazim Kemal Ekenel
发表日期
2016/9/21
研讨会论文
2016 International Conference of the Biometrics Special Interest Group (BIOSIG)
页码范围
1-6
出版商
IEEE
简介
Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for age and gender classification. The generic AlexNet-like architecture and domain specific VGG-Face CNN model are employed and fine-tuned with the Adience dataset prepared for age and gender classification in uncontrolled environments. In addition, task specific GilNet CNN model has also been utilized and used as a baseline method in order to compare with transferred models. Experimental results show that both …
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G Ozbulak, Y Aytar, HK Ekenel - 2016 International Conference of the Biometrics …, 2016