Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
This work addresses the need for efficient age and gender classification on mobile devices, but it is incremental as it builds upon existing multi-task CNN methods with optimizations for lightweight deployment.
The paper tackled the problem of age and gender classification from unconstrained face images on mobile devices with limited computing power, and the result was a lightweight multi-task CNN that achieved better accuracy than baseline methods on the Adience dataset.
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.