TY - GEN
T1 - Age and gender classification from ear images
AU - Yaman, Dogucan
AU - Eyiokur, Fevziye Irem
AU - Sezgin, Nurdan
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2018 IEEE-CONFERENCE. All rights reserved.
PY - 2018/6/29
Y1 - 2018/6/29
N2 - In this paper, we present a detailed analysis on extracting soft biometric traits, age and gender, from ear images. 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. In the study, we have utilized both geometric features and appearancebased features for ear representation. The utilized geometric features are based on eight anthropometric landmarks and consist of 14 distance measurements and two area calculations. The appearance-based methods employ deep convolutional neural networks for representation and classification. The well-known convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and SqueezeNet have been adopted for the study. They have been fine-tuned on a large-scale ear dataset that has been built from the profile and close-to-profile face images in the Multi-PIE face dataset. This way, we have performed a domain adaptation. The updated models have been fine-tuned once more time on the small-scale target ear dataset, which contains only around 270 ear images for training. According to the experimental results, appearance-based methods have been found to be superior to the methods based on geometric features. We have achieved 94% accuracy for gender classification, whereas 52% accuracy has been obtained for age classification. These results indicate that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.
AB - In this paper, we present a detailed analysis on extracting soft biometric traits, age and gender, from ear images. 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. In the study, we have utilized both geometric features and appearancebased features for ear representation. The utilized geometric features are based on eight anthropometric landmarks and consist of 14 distance measurements and two area calculations. The appearance-based methods employ deep convolutional neural networks for representation and classification. The well-known convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and SqueezeNet have been adopted for the study. They have been fine-tuned on a large-scale ear dataset that has been built from the profile and close-to-profile face images in the Multi-PIE face dataset. This way, we have performed a domain adaptation. The updated models have been fine-tuned once more time on the small-scale target ear dataset, which contains only around 270 ear images for training. According to the experimental results, appearance-based methods have been found to be superior to the methods based on geometric features. We have achieved 94% accuracy for gender classification, whereas 52% accuracy has been obtained for age classification. These results indicate that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.
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U2 - 10.1109/IWBF.2018.8401568
DO - 10.1109/IWBF.2018.8401568
M3 - Conference contribution
AN - SCOPUS:85050524232
T3 - IWBF 2018 - Proceedings: 2018 6th International Workshop on Biometrics and Forensics
SP - 1
EP - 7
BT - IWBF 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Workshop on Biometrics and Forensics, IWBF 2018
Y2 - 7 June 2018 through 8 June 2018
ER -