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Landmark-Guided Local Deep Neural Networks for Age and Gender Classification
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition.
Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive.
In this paper, we propose to construct a local deep neural network for age and gender classification.
In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training.
A holistical edge map for an entire image is also used for training a “global” network.
The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks.
Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.
American Psychological Association (APA)
Zhang, Yungang& Xu, Tianwei. 2018. Landmark-Guided Local Deep Neural Networks for Age and Gender Classification. Journal of Sensors،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1201511
Modern Language Association (MLA)
Zhang, Yungang& Xu, Tianwei. Landmark-Guided Local Deep Neural Networks for Age and Gender Classification. Journal of Sensors No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1201511
American Medical Association (AMA)
Zhang, Yungang& Xu, Tianwei. Landmark-Guided Local Deep Neural Networks for Age and Gender Classification. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1201511
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201511