Age and Gender Detection using Deep Learning Models

2019 ◽  
Vol 7 (4) ◽  
pp. 671-676
Author(s):  
Arsala Kadri ◽  
Kirti Sharma ◽  
Narendrasinh Chauhan
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Age And Gender ◽  
And Gender ◽  
Gender Detection

Age and Gender Detection System using Raspberry Pi

2019 ◽  
Vol 7 (6) ◽  
pp. 14-18
Author(s):  
Sumangala Biradar ◽  
Beena Torgal ◽  
Namrata Hosamani ◽  
Renuka Bidarakundi ◽  
Shruti Mudhol
Keyword(s):  
Detection System ◽  
Raspberry Pi ◽  
Age And Gender ◽  
And Gender ◽  
Gender Detection

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2424 ◽  
Author(s):  
Md Atiqur Rahman Ahad ◽  
Thanh Trung Ngo ◽  
Anindya Das Antar ◽  
Masud Ahmed ◽  
Tahera Hossain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Prediction Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Training Dataset ◽  
Wearable Sensor ◽  
Age And Gender ◽  
Test Dataset ◽  
And Gender

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

scholarly journals Age and Gender Detection Using CNN

2021 ◽  
pp. 29-33
Author(s):  
Prof. Jaydeep Patil ◽  
Rohit Thombare ◽  
Yash deo ◽  
Rohit Kharche ◽  
Nikhil Tagad
Keyword(s):  
Real World ◽  
Working Environment ◽  
Age And Gender ◽  
The Real ◽  
Lighting Conditions ◽  
And Gender ◽  
Gender Detection ◽  
World Environment ◽  
Age Estimates

In recent years, much effort has been put forth to balance age and sexuality. It has been reported that the age can be accurately measured under controlled areas such as front faces, no speech, and stationary lighting conditions. However, it is not intended to achieve the same level of accuracy in the real world environment due to the wide variation in camera use, positioning, and lighting conditions. In this paper, we use a recently proposed mechanism to study equipment called covariate shift adaptation to reduce the change in lighting conditions between the laboratory and the working environment. By examining actual age estimates, we demonstrate the usefulness of our proposed approach.

scholarly journals AGE AND GENDER DETECTION FROM LIVE CAMERAS FOR FUTURE SMART SHOPPING STORES.

2021 ◽  
Vol 99 (07) ◽  
pp. 56-61
Author(s):  
Jamoliddin Uraimov ◽  
◽  
Nosirjon Abdurazaqov ◽  
Keyword(s):  
Age And Gender ◽  
And Gender ◽  
Gender Detection

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

scholarly journals An Examination of Fairness of AI Models for Deepfake Detection

2021 ◽  
Author(s):  
Loc Trinh ◽  
Yan Liu
Keyword(s):  
Deep Learning ◽  
Error Rate ◽  
X Rays ◽  
Learning Models ◽  
Race And Gender ◽  
And Gender

Recent studies have demonstrated that deep learning models can discriminate based on protected classes like race and gender. In this work, we evaluate bias present in deepfake datasets and detection models across protected subgroups. Using facial datasets balanced by race and gender, we examine three popular deepfake detectors and find large disparities in predictive performances across races, with up to 10.7% difference in error rate between subgroups. A closer look reveals that the widely used FaceForensics++ dataset is overwhelmingly composed of Caucasian subjects, with the majority being female Caucasians. Our investigation of the racial distribution of deepfakes reveals that the methods used to create deepfakes as positive training signals tend to produce ``irregular" faces - when a person’s face is swapped onto another person of a different race or gender. This causes detectors to learn spurious correlations between the foreground faces and fakeness. Moreover, when detectors are trained with the Blended Image (BI) dataset from Face X-Rays, we find that those detectors develop systematic discrimination towards certain racial subgroups, primarily female Asians.

Age and gender detection in the I-DASH project

2011 ◽  
Vol 7 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Hugo Meinedo ◽  
Isabel Trancoso
Keyword(s):  
Age And Gender ◽  
And Gender ◽  
Gender Detection
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