Age and gender estimation of unfiltered faces

Eran Eidinger, Roee Enbar, Tal Hassner

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


This paper concerns the estimation of facial attributes - namely, age and gender - from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research - absence of data - we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

اللغة الأصليةالإنجليزيّة
رقم المقال6906255
الصفحات (من إلى)2170-2179
عدد الصفحات10
دوريةIEEE Transactions on Information Forensics and Security
مستوى الصوت9
رقم الإصدار12
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 ديسمبر 2014

ملاحظة ببليوغرافية

Publisher Copyright:
© 2014 IEEE.


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