Papers › Age and Gender Classification using Convolutional Neural Networks
Age and Gender Classification using Convolutional Neural Networks
Gil Levi, Tal Hassner
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Age And Gender Classification | Adience Age | Levi_Hassner CNN (over-sample, caffe) | Accuracy (5-fold) | 50.7 | #13 of 16 | Archive leaderboard | report |
| Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, caffe) | Accuracy (5-fold) | 49.5 | #14 of 16 | Archive leaderboard | report |
| Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, tensorflow) | Accuracy (5-fold) | 44.14 | #16 of 16 | Archive leaderboard | report |
| Age And Gender Classification | Adience Gender | Levi_Hassner CNN ( over-sample, caffe) | Accuracy (5-fold) | 86.8 | #7 of 10 | Archive leaderboard | report |
| Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, caffe) | Accuracy (5-fold) | 85.9 | #8 of 10 | Archive leaderboard | report |
| Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, tensorflow) | Accuracy (5-fold) | 82.52 | #10 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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