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Double channel CNN for accurate age and gender estimation in complex scenarios
Author(s): Xiuhong Zhu; Shanjun Li; Guoyou Wang; Lanfang Kong; Zhaoyang Hu
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Paper Abstract

In recent years, the application of age and gender estimation from face images are becoming increasingly wider and deeper. Existing age and gender estimation pipelines usually process images through machine learning, like SVM, AdaBoost and etc. However, the performance gain of such method is usually limited to handle images with strict conditions or simple backgrounds. At present, age and gender estimation in an open environment still face enormous challenges. In this paper, we introduce a method based on double channel convolutional neural network (CNN) for accurate age and gender estimation in complex scenarios. To start with, detecting face regions with single-face or multifaces. Secondly, utilizing the face alignment based on the facial landmark detection. Finally, using double channel CNN structure with Xgboost to train the model for age and gender estimation. Experiments show that the proposed method based on double channel CNN can achieve a higher accuracy at comparable time cost compared with single channel CNN method and is robust to face images from wild conditions.

Paper Details

Date Published: 9 August 2018
PDF: 13 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061O (9 August 2018); doi: 10.1117/12.2503070
Show Author Affiliations
Xiuhong Zhu, Huazhong Univ. of Science and Technology (China)
Shanjun Li, Huazhong Univ. of Science and Technology (China)
Guoyou Wang, Huazhong Univ. of Science and Technology (China)
Lanfang Kong, Huazhong Univ. of Science and Technology (China)
Zhaoyang Hu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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