Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications

6 Jun 2018  ·  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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Age And Gender Classification Adience Age LMTCNN-2-1 (single crop, tensorflow) Accuracy (5-fold) 44.26 # 13
Age And Gender Classification Adience Gender LMTCNN-2-1 (single crop, tensorflow) Accuracy (5-fold) 85.16 # 7