תקציר
Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.
שפה מקורית | אנגלית |
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כותר פרסום המארח | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 |
מוציא לאור | IEEE Computer Society |
עמודים | 34-42 |
מספר עמודים | 9 |
מסת"ב (אלקטרוני) | 9781467367592 |
מזהי עצם דיגיטלי (DOIs) | |
סטטוס פרסום | פורסם - 19 אוק׳ 2015 |
אירוע | IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 - Boston, ארצות הברית משך הזמן: 7 יוני 2015 → 12 יוני 2015 |
סדרות פרסומים
שם | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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כרך | 2015-October |
ISSN (מודפס) | 2160-7508 |
ISSN (אלקטרוני) | 2160-7516 |
כנס
כנס | IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 |
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מדינה/אזור | ארצות הברית |
עיר | Boston |
תקופה | 7/06/15 → 12/06/15 |
הערה ביבליוגרפית
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