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.
|Title of host publication||2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|State||Published - 19 Oct 2015|
|Event||IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 - Boston, United States|
Duration: 7 Jun 2015 → 12 Jun 2015
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Conference||IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015|
|Period||7/06/15 → 12/06/15|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
- Benchmark testing
- Computer architecture
- Face recognition