The Unconstrained Ear Recognition Challenge

Emeršič, Žiga; Štepec, Dejan; Peer, Peter; George, Anjith; Ahmad, Adil; Omar, Elshibani; Boult, Terrance E.; Safdari, Reza; Zhou, Yonggang; Zafeiriou, Stefanos; Yaman, Dogucan; Eyiokur, Fevziye Irem; Ekenel, Hazım Kemal

doi:10.48550/arxiv.1708.06997

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ear biometrics has become a popular research topic [1]. A recent challenge, named as Unconstrained Ear Recognition Challenge [2] has shown the difficulties of performing person identification from ear images in the wild. To complement the identity related information from ear images, utilizing soft biometric traits, such as age and gender information can be auxiliary.…”

Section: Introductionmentioning

confidence: 99%

Age and gender classification from ear images

Yaman

Eyiokur

Sezgin

et al. 2018

2018 International Workshop on Biometrics and Forensics (IWBF)

Self Cite

View full text Add to dashboard Cite

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 appearance-based 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.

show abstract

Section: Introductionmentioning

confidence: 99%