  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.
