https://scholars.lib.ntu.edu.tw/handle/123456789/581328
Title: | Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications | Authors: | Lee J.-H Chan Y.-M Chen T.-Y CHU-SONG CHEN |
Keywords: | Convolution; Deep learning; Mobile computing; Mobile devices; Neural networks; Attribute analysis; Computing power; Convolutional Neural Networks (CNN); Gender classification; Joint estimation; Mobile applications; Multitask learning; Neuron networks; Classification (of information) | Issue Date: | 2018 | Start page/Pages: | 162-165 | Source: | Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018 | 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. ? 2018 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050087068&doi=10.1109%2fMIPR.2018.00036&partnerID=40&md5=221754aced0a31e801338c9971a4afef https://scholars.lib.ntu.edu.tw/handle/123456789/581328 |
DOI: | 10.1109/MIPR.2018.00036 |
Appears in Collections: | 資訊工程學系 |
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