Face-Based Age and Gender Classification Using Deep Learning Model

2020 ◽  
pp. 125-137 ◽  
Author(s):  
Olatunbosun Agbo-Ajala ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender ◽  
Deep Learning Model

scholarly journals GRANet: A Deep Learning Model for Classification of Age and Gender from Facial Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Avishek Garain ◽  
Biswarup Ray ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Norazak Senu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Age And Gender ◽  
And Gender ◽  
Facial Images ◽  
Deep Learning Model

scholarly journals Masked Face Analysis via Multi-Task Deep Learning

2021 ◽  
Vol 7 (10) ◽  
pp. 204
Author(s):  
Vatsa S. Patel ◽  
Zhongliang Nie ◽  
Trung-Nghia Le ◽  
Tam V. Nguyen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Unified Framework ◽  
Improve Performance ◽  
Face Analysis ◽  
And Gender ◽  
Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

Age and Gender Classification Based on Deep Learning

2021 ◽  
pp. 425-437
Author(s):  
Tejas Agarwal ◽  
Mira Andhale ◽  
Anand Khule ◽  
Rushikesh Borse
Keyword(s):  
Deep Learning ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender

A hybrid deep learning CNN–ELM for age and gender classification

2018 ◽  
Vol 275 ◽  
pp. 448-461 ◽  
Author(s):  
Mingxing Duan ◽  
Kenli Li ◽  
Canqun Yang ◽  
Keqin Li
Keyword(s):  
Deep Learning ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model
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