Papers › Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight...
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
Jia-Hong Lee, Yi-Ming Chan, Ting-Yen Chen, Chu-Song Chen
Automatic age and gender classification based on unconstrained images has become essential techniques on mobile devices. With limited computing power, how to develop a robust system becomes a challenging task. In this paper, we present an efficient convolutional neural network (CNN) called lightweight multi-task CNN for simultaneous age and gender classification. Lightweight multi-task CNN uses depthwise separable convolution to reduce the model size and save the inference time. On the public challenging Adience dataset, the accuracy of age and gender classification is better than baseline multi-task CNN 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 | LMTCNN-2-1 (single crop, tensorflow) | Accuracy (5-fold) | 44.26 | #15 of 16 | Archive leaderboard | report |
| Age And Gender Classification | Adience Gender | LMTCNN-2-1 (single crop, tensorflow) | Accuracy (5-fold) | 85.16 | #9 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.
Methods
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