A hybrid approach of neural networks for age and gender classification through decision fusion

2021 ◽  
Vol 66 ◽  
pp. 102459
Author(s):  
James Rwigema ◽  
Joseph Mfitumukiza ◽  
Kim Tae-Yong
Keyword(s):  
Neural Networks ◽  
Hybrid Approach ◽  
Decision Fusion ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender

scholarly journals Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yungang Zhang ◽  
Tianwei Xu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Global Network ◽  
Gender Classification ◽  
Local Networks ◽  
Age And Gender ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Model Training ◽  
And Gender

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.

Age and Gender Recognition from Speech Using Deep Neural Networks

2020 ◽  
pp. 332-344
Author(s):  
Héctor A. Sánchez-Hevia ◽  
Roberto Gil-Pita ◽  
Manuel Utrilla-Manso ◽  
Manuel Rosa-Zurera
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Gender Recognition ◽  
Age And Gender ◽  
And Gender
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