Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
2018-06-06Code Available0· sign in to hype
Jia-Hong Lee, Yi-Ming Chan, Ting-Yen Chen, Chu-Song Chen
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Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Adience Age | LMTCNN-2-1 (single crop, tensorflow) | Accuracy (5-fold) | 44.26 | — | Unverified |
| Adience Gender | LMTCNN-2-1 (single crop, tensorflow) | Accuracy (5-fold) | 85.16 | — | Unverified |