Spectral Clustered Rayleigh Classifier Technique for Face and Gender Detection

2016 ◽  
Vol 13 (12) ◽  
pp. 9183-9189
Author(s):  
S Chitra ◽  
G Balakrishnan
Keyword(s):  
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 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

scholarly journals Comparison of FairMOT-VGG16 and MCMOT Implementation for Multi-Object Tracking and Gender Detection on Mall CCTV

2021 ◽  
Vol 14 (1) ◽  
pp. 49-64
Author(s):  
Pray Somaldo ◽  
Dina Chahyati
Keyword(s):  
Detection System ◽  
Tracking System ◽  
Shopping Center ◽  
Specific Information ◽  
Gender Classification ◽  
Person Detection ◽  
Men And Women ◽  
Multi Person Tracking ◽  
And Gender ◽  
Gender Detection

The crowd detection system on CCTV has proven to be useful for retail and shopping sector owners in mall areas. The data can be used as a guide by shopping center owners to find out the number of visitors who enter at a certain time. However, such information was still insufficient. The need for richer data has led to the development of more specific person detection which involves gender. Gender detection can provide specific information on the number of men and women visiting a particular location. However, gender detection alone does not provide an identity label for every detection that occurs, so it needs to be combined with a multi-person tracking system. This study compares two tracking methods with gender detection, namely FairMOT with gender classification and MCMOT. The first method produces MOTA, MOTP, IDS, and FPS of 78.56, 79.57, 19, and 24.4, while the second method produces 69.84, 81.94, 147, and 30.5. In addition, evaluation of gender was also carried out where the first method resulted in a gender accuracy of 65\% while the second method was 62.35\%. 

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 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

scholarly journals Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

2021 ◽  
Vol 109 (4) ◽  
Author(s):  
Paul Sebo
Keyword(s):  
Performance Metrics ◽  
Data Manipulation ◽  
Detection Tool ◽  
Overall Performance ◽  
And Gender ◽  
Gender Detection ◽  
Simple Manipulation

Objective: We recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database.Methods: We used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded).Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%).Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.

scholarly journals Computational appraisal of gender representativeness in popular movies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Antoine Mazières ◽  
Telmo Menezes ◽  
Camille Roth
Keyword(s):  
Large Scale ◽  
Temporal Trend ◽  
Scale Up ◽  
Detection Algorithm ◽  
Representation Of Women ◽  
Mise En Scene ◽  
Spatial Composition ◽  
And Gender ◽  
Gender Detection ◽  
User Ratings

AbstractGender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men. Beyond the confirmation of a strong under-representation of women, we exhibit a clear temporal trend towards fairer representativeness. We further contrast our findings with respect to a movie genre, budget, and various audience-related features such as movie gross and user ratings. We lastly propose a fine description of significant asymmetries in the mise-en-scène and mise-en-cadre of characters in relation to their gender and the spatial composition of a given frame.

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