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 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.

scholarly journals Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users

2020 ◽  
Vol 10 (17) ◽  
pp. 5957 ◽  
Author(s):  
Roberto Garcia-Guzman ◽  
Yair A. Andrade-Ambriz ◽  
Mario-Alberto Ibarra-Manzano ◽  
Sergio Ledesma ◽  
Juan Carlos Gomez ◽  
...  
Keyword(s):  
Text Analysis ◽  
State Of The Art ◽  
Essential Feature ◽  
User Profile ◽  
Age And Gender ◽  
Category Information ◽  
Gender And Age ◽  
Twitter Users ◽  
And Gender ◽  
Number Of Publications

Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users’ activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people’s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users’ age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users’ profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user’s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications.

scholarly journals Family-Aware Convolutional Neural Network for Image-based Kinship Verification

2020 ◽  
Vol 13 (6) ◽  
pp. 20-30
Author(s):  
Reza Rachmadi ◽  
◽  
I Purnama ◽  
Supeno Nugroho ◽  
Yoyon Suprapto ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
The State ◽  
Recognition Model ◽  
Kinship Verification ◽  
The Family ◽  
Verification Problem ◽  
The Face

Faces is a unique region in our body that can be used as a biometric identity. Furthermore, the face between two people that have a kinship relationship may share the same face features which can be used to decide whether two people have a kinship relationship or not. In this paper, we proposed a family-aware convolutional neural network (CNN) for the visual kinship verification problem. Our proposed classifier is constructed by paralleling the state-of-the-art face recognition model and attaching two additional networks, a family-aware network, and a kinship verification network. The family-aware network weights adjusted by learning features specific to the family using deep metric learning loss while the kinship verification network use softmax loss to learn the kinship verification problem. One of the advantages of our proposed classifier is that the output of the classifier is normalized and can be represented as the probability of two images being kin or non-kin. To preserve the face recognition features extraction ability in the state-of-the-art face recognition model, we freeze the weights of the convolutional layers in the classifier for the training process. In the testing process, the family-aware network is detached to construct the final classifier. Experiments on FIW (Families In the Wild) dataset show that our proposed classifier performs better comparing with classifiers that trained without a family-aware network and the ensemble version of the classifier is comparable with several state-of-the-art methods with an average accuracy of 68.84%.

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 Learning a Genetic Measure for Kinship Verification Using Facial Images

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Lu Kou ◽  
Xiuzhuang Zhou ◽  
Min Xu ◽  
Yuanyuan Shang
Keyword(s):  
Similarity Measure ◽  
Genetic Similarity ◽  
State Of The Art ◽  
Experimental Results ◽  
Facial Appearance ◽  
Similarity Learning ◽  
Kinship Verification ◽  
Practical Applications ◽  
In The Wild ◽  
Facial Images

Motivated by the key observation that children generally resemble their parents more than other persons with respect to facial appearance, distance metric (similarity) learning has been the dominant choice for state-of-the-art kinship verification via facial images in the wild. Most existing learning-based approaches to kinship verification, however, are focused on learning a genetic similarity measure in a batch learning manner, leading to less scalability for practical applications with ever-growing amount of data. To address this, we propose a new kinship verification approach by learning a sparse similarity measure in an online fashion. Experimental results on the kinship datasets show that our approach is highly competitive to the state-of-the-art alternatives in terms of verification accuracy, yet it is superior in terms of scalability for practical applications.

Sign in / Sign up

Export Citation Format

Share Document