Age and Gender Prediction Using Convolutional Neural Networks

2018 ◽  
Author(s):  
Emre Safak ◽  
Necaattin Bariicc
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age And Gender ◽  
And Gender

scholarly journals A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 328 ◽  
Author(s):  
Khalil Khan ◽  
Muhammad Attique ◽  
Rehan Ullah Khan ◽  
Ikram Syed ◽  
Tae-Sun Chung
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Face Image ◽  
Deep Convolutional Neural Networks ◽  
Age And Gender ◽  
Face Image Analysis ◽  
Probability Maps ◽  
End To End ◽  
And Gender

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

scholarly journals Evolutionary-Fuzzy-Integral-Based Convolutional Neural Networks for Facial Image Classification

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 997 ◽  
Author(s):  
Lin ◽  
Lin ◽  
Sun ◽  
Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Optimization Methods ◽  
Optimization Method ◽  
Fuzzy Integral ◽  
Network Architectures ◽  
Age And Gender ◽  
Density Values ◽  
And Gender

Various optimization methods and network architectures are used by convolutional neural networks (CNNs). Each optimization method and network architecture style have their own advantages and representation abilities. To make the most of these advantages, evolutionary-fuzzy-integral-based convolutional neural networks (EFI-CNNs) are proposed in this paper. The proposed EFI-CNNs were verified by way of face classification of age and gender. The trained CNNs’ outputs were set as inputs of a fuzzy integral. The classification results were operated using either Sugeno or Choquet output rules. The conventional fuzzy density values of the fuzzy integral were decided by heuristic experiments. In this paper, particle swarm optimization (PSO) was used to adaptively find optimal fuzzy density values. To combine the advantages of each CNN type, the evaluation of each CNN type in EFI-CNNs is necessary. Three CNN structures, AlexNet, very deep convolutional neural network (VGG16), and GoogLeNet, and three databases, computational intelligence application laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in experiments to classify age and gender. The experimental results show that the proposed method achieved 5.95% and 3.1% higher accuracy, respectively, in classifying age and gender.

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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