Age and Gender Prediction using Deep Convolutional Neural Networks

2019 ◽  
Author(s):  
Insha Rafique ◽  
Awais Hamid ◽  
Sheraz Naseer ◽  
Muhammad Asad ◽  
Muhammad Awais ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep 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.

scholarly journals Age and Gender Prediction using Face Recognition

2021 ◽  
Vol 11 (2) ◽  
pp. 48-51
Author(s):  
Sai Teja Challa ◽  
◽  
Sowjanya Jindam ◽  
Ruchitha Reddy Reddy ◽  
Kalathila Uthej ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Gender Recognition ◽  
Deep Convolutional Neural Networks ◽  
Age And Gender ◽  
Face Images ◽  
Wide Range ◽  
Frame Work ◽  
And Gender ◽  
Learning Data

Automatic age and gender prediction from face images has lately attracted much attention due to its wide range of applications in numerous facial analyses. We show in this study that utilizing the Caffe Model Architecture of Deep Learning Frame Work; we were able to greatly enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a much simpler convolutional net architecture that can be employed even if no learning data is available. In a recent study presenting a potential benchmark for age and gender estimation, we show that our strategy greatly outperforms existing state-of-the-art methods.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Sign in / Sign up

Export Citation Format

Share Document