Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Sajid, Muhammad; Shafique, Tamoor; Manzoor, Sohaib; Iqbal, Faisal; Talal, Hassan; Qureshi, Usama Samad; Riaz, Imran

doi:10.3390/sym10050148

Cited by 13 publications

(14 citation statements)

References 47 publications

(94 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the impact of demographic traits (gender and ethnicity) on asymmetric dimensions, we selected two standard face image datasets, called MORPH II (termed as MORPH in the rest of the study) [17] and FERET [18]. These datasets have been used extensively in demographic estimation and recognition tasks such as [7,8,19]. The MORPH dataset contains 55,000 unique face images of more than 13,000 subjects.…”

Section: Image Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity

et al. 2018

Self Cite

View full text Add to dashboard Cite

Bilateral facial asymmetry is frequently exhibited by humans but its combined evaluation across demographic traits including gender and ethnicity is still an open research problem. In this study we measure and evaluate facial asymmetry across gender and different ethnic groups and investigate the differences in asymmetric facial dimensions among the subjects from two public face datasets, the MORPH and FERET. To this end, we detect 28 facial asymmetric dimensions from each face image using an anthropometric technique. An exploratory analysis is then performed via a multiple linear regression model to determine the impact of gender and ethnicity on facial asymmetry. Post-hoc Tukey test has been used to validate the results of the proposed method. The results show that out of 28 asymmetric dimensions, females differ in 25 dimensions from males. African, Asian, Hispanic and other ethnic groups have asymmetric dimensions that differ significantly from those of Europeans. These findings could be important to certain applications like the design of facial fits, as well as guidelines for facial cosmetic surgeons. Lastly, we train a neural network classifier that employs asymmetric dimensions for gender and race classification. The experimental results show that our trained classifier outperforms the support vector machine (SVM) and k-nearest neighbors (kNN) classifiers.

show abstract

Section: Image Datasetsmentioning

confidence: 99%

“…The knowledge learned from age group estimation is then used to recognize face images across aging variations. Facial asymmetry-based descriptors have been used in [8] for demographic estimation and face recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [99], demographic traits including age group, gender, and race have been used to enhance the recognition accuracies of face images across challenging aging variations. First, the convolutional neural networks are used to extract age-, gender-, and race-specific face features.…”

Section: Feature Extraction Techniques For Face Recognitionmentioning

confidence: 99%

“…In the above-presented studies [98][99][100][101][102][103][104][105], handcrafted and deeply learned face features have been introduced for robust face recognition.…”

Section: Feature Extraction Techniques For Face Recognitionmentioning

confidence: 99%

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

Self Cite

232

View full text Add to dashboard Cite

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

show abstract

“…They used the FERET and ORL face databases. Sajid et al [26] extracted facial-asymmetry-based demographic informative features to evaluate the age group, gender, and race of a given face image by employing two well-known face datasets, MORPH II and FERET. Wang et al [27] proposed the deep learning technique to obtain facial landmark detection and limitless face recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

View full text Add to dashboard Cite

Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.

show abstract

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Cited by 13 publications

References 47 publications

Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity

Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

Contact Info

Product

Resources

About