scholarly journals Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

2021 ◽  
Vol 7 ◽  
pp. e735
Author(s):  
Nermeen Nader ◽  
Fatma El-Zahraa El-Gamal ◽  
Shaker El-Sappagh ◽  
Kyung Sup Kwak ◽  
Mohammed Elmogy
Keyword(s):  
Deep Learning ◽  
Metric Learning ◽  
Future Research ◽  
Age And Gender ◽  
Kinship Verification ◽  
Gender Variation ◽  
Age Variations ◽  
Feature Based ◽  
And Gender ◽  
Facial Images

Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

Geometric and Textural Cues for Automatic Kinship Verification

2015 ◽  
Vol 29 (03) ◽  
pp. 1556001 ◽  
Author(s):  
Andrea Bottino ◽  
Tiago Figueiredo Vieira ◽  
Ihtesham Ul Islam
Keyword(s):  
State Of The Art ◽  
Challenging Problem ◽  
Age And Gender ◽  
Kinship Verification ◽  
Verification Problem ◽  
Experimental Protocols ◽  
Textural Data ◽  
And Gender ◽  
Computer Identification ◽  
Facial Images

Automatic Kinship verification aims at recognizing the degree of kinship of two individuals from their facial images and it has possible applications in image retrieval and annotation, forensics and historical studies. This is a recent and challenging problem, which must deal with different degrees of kinship and variations in age and gender. Our work explores the computer identification of parent–child pairs using a combination of (i) features of different natures, based on geometric and textural data, (ii) feature selection and (iii) state-of-the-art classifiers. Experiments show that the proposed approach provides a valuable solution to the kinship verification problem, as suggested by its comparison with different methods on the same data and the same experimental protocols. We further show the good generalization capabilities of our method in several cross-database experiments.

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 The Influence of Architecture Students’ Learning Approaches on their Academic Performance in Two Nigeria Universities

2021 ◽  
Vol 20 (2) ◽  
pp. 137-151
Author(s):  
Gabriel Sen ◽  
Albert Adeboye ◽  
Oluwole Alagbe
Keyword(s):  
Regression Analysis ◽  
Academic Performance ◽  
Deep Learning ◽  
Pilot Study ◽  
Statistical Analyses ◽  
Future Research ◽  
Learning Approaches ◽  
Research Needs ◽  
Heterogeneous Samples ◽  
And Gender

The paper was a pilot study that examined learning approaches of architecture students; variability of approaches by university type and gender and; influence of architecture students’ learning approaches on their academic performance. The sample was 349 architecture students from two universities. Descriptive and statistical analyses were used. Results revealed predominant use of deep learning approaches by students. Furthermore, learning approaches neither significantly differed by university type nor gender. Regression analysis revealed that demographic factors accounted for 2.9% of variation in academic performance (F (2,346) = 6.2, p = 0.002, R2 = 0.029, f2 = 0.029) and when learning approaches were also entered the model accounted for 4.4% of variation in academic performance (F (14,334) =2.2, p =0.009, R2 = 0.044, f2=0.044). Deep learning approaches significantly and positively influenced variation in academic performance while surface learning approaches significantly and negatively influenced academic performance. This implies that architectural educators should use instructional methods that encourage deep approaches. Future research needs to use larger and more heterogeneous samples for confirmation of results.

A study on the use of ‘-tayi’ type expressions in Daegu dialect: With focused on context, age, and gender variation

2021 ◽  
Vol 92 ◽  
pp. 81-109
Author(s):  
Yugyeong Park ◽  
Okhui Lee
Keyword(s):  
Age And Gender ◽  
Gender Variation ◽  
And Gender

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.

Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval

2016 ◽  
Author(s):  
Yaron Anavi ◽  
Ilya Kogan ◽  
Elad Gelbart ◽  
Ofer Geva ◽  
Hayit Greenspan
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Age And Gender ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
And Gender

scholarly journals Age and Gender Differences in Experience with and Connectedness to Nature Among Children

2021 ◽  
Vol 6 (16) ◽  
Author(s):  
Nor Diyana Mustapa ◽  
Khalilah Hassan ◽  
Siti Nuratirah Che Mohd Nasir ◽  
Wenny Arminda
Keyword(s):  
Gender Differences ◽  
Publishing House ◽  
Future Research ◽  
Rural Children ◽  
Age And Gender ◽  
Connectedness To Nature ◽  
Age And Gender Differences ◽  
International Publishing ◽  
And Gender ◽  
Open Access Article

This study aims to identify the age and gender differences in children's experiences with nature and their connectedness to nature (CTN). This study employed a quantitative approach and involved 760 children aged 10-11 years old, including urban and rural children in Kedah and Pulau Pinang. Questionnaires were distributed to children at schools. Findings suggest that age and gender do influence the frequency of children having experiences with nature as well as their CTN. The directions for future research are also discussed. Keywords: experiences with nature; connectedness to nature; age; gender eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI:

Adolescent Victim Experiences of Cyberbullying

2019 ◽  
pp. 236-254
Author(s):  
Minghui Gao ◽  
Tonja Filipino ◽  
Xu Zhao ◽  
Mark McJunkin
Keyword(s):  
Age Differences ◽  
Research Study ◽  
Mental Wellbeing ◽  
Future Research ◽  
Age And Gender ◽  
Gender And Age ◽  
Research Findings ◽  
Gender And Age Differences ◽  
And Gender

This chapter started by introducing a recent research study that disclosed adolescent victim experiences across seven major types of cyberbullying, significant gender and age differences, and reasons for not reporting incidents of cyberbullying to adults. The chapter then related the research findings to major areas in the literature on the nature and forms of cyberbullying in contrast to traditional forms of bullying, its prevalence among school-aged youths, the effects of gender and age on adolescent victim experiences of cyberbullying, and the factors that contribute to adolescent attitude toward reporting cyberbullying incidents to adults. The chapter suggested that future research should further explore issues such as how various types of cyberbullying affect adolescent mental wellbeing, how age and gender affect school-aged youth victim experiences of various forms of cyberbullying, and how professionals and other adults may help adolescents counter cyberbullying.

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
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