Perform Gender Classification Research Articles

Impact of Occlusion Masks on Gender Classification from Iris Texture

Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

A SURVEY OF FACIAL GENDER CLASSIFICATION

: Gender is the most critical demographic attribute of human beings. Gender classification from facial images is widely used in computer vision for surveillance, a straightforward task for humans, and a challenging task for machines. This paper provides a study of facial gender classification in the field of computer vision. We highlight the challenges faced during the image capturing process due to factors such as illumination, angle, occlusion, and expressions. We also review various feature extraction approaches used by previous researchers to perform the facial gender classification task. This paper also compares the performances of previous methods on various face datasets to perform gender classification. We observed from the previous studies that good performance had been achieved for a dataset of facial images taken in a controlled environment. However, much more work needs to be done to increase the accuracy and robustness of facial gender classification, especially in uncontrolled environments.

Eigenface and SIFT For Gender Classification

In this paper, we study and analyze five different approaches for gender classification (Male or Female). The purpose of approaches is to extract the main features from face image and for each approach; we used these features as input to the Support Vector Machine SVM classifier for classification process. In the first approach, we implemented the Principal Component Analysis PCA features as input to the SVM classifier. In the second approach, the resulted parameters of Scale Invariant Features Transform approach are used as input to SVM classifier. In the third approach, we implemented Eigenfaces as input to SIFT, and then the results of SIFT are used as input to SVM classifier also. In the fourth approach, we implemented Eigenfaces to be input to Volume-SIFT (VSIFT) and then used as input to SVM classifier. The last, we modified the VSIFT approach and use as input to SVM classifier. The practical implementation results show that the proposed approach (modified VSIFT) gave us high performance of gender classification than other approaches.The training and testing images set consist of number of faces which are obtained from (Computer Vision Science Research Projects).The aim of this paper is to analyze and discuss the results of applying the eigenfaces into the new proposed approach of modifying Volume-SIFT for classifying the faces into its gender.

'Harmless' adversarial network harmonization approach for removing site effects and improving reproducibility in neuroimaging studies.

Multi-site collaboration, which gathers together samples from multiple sites, is a powerful way to overcome the small-sample problem in the neuroimaging field and has the potential to discover more robust and reproducible biomarkers. However, confounds among the datasets caused by various site-specific factors may dramatically reduce the cross-site reproducibility performance. To properly remove confounds while improving cross-site task performances, we propose a maximum classifier discrepancy generative adversarial network (MCD-GAN) that combines the advantages of generative models and maximum discrepancy theory. The mechanisms of MCD-GAN and how it harmonizes the dataset are visualized using simulated data. The performance of MCD-GAN was also compared with state-of-the-art methods (e.g., ComBat, cycle-GAN) within Adolescent Brain Cognitive Development (ABCD) dataset. Result demonstrates that the proposed MCD-GAN can effectively improve the cross-site gender classification performance by harmonizing site effects. Our proposed framework is also suitable for various classification/prediction tasks and is promising to facilitate the cross-site reproducibility of neuroimaging studies. Clinical Relevance- This work provides an efficient method for removing sites effects and improving reproducibility in large-cohort neuroimaging studies.

Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization

Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model.

An exploration of gender gap using advanced data science tools: actuarial research community

This paper explores the role of gender gap in the actuarial research community with advanced data science tools. The web scraping tools were employed to create a database of publications that encompasses six major actuarial journals. This database includes the article names, authors’ names, publication year, volume, and the number of citations for the time period 2005–2018. The advanced tools built as part of the R software were used to perform gender classification based on the author’s name. Further, we developed a social network analysis by gender in order to analyze the collaborative structure and other forms of interaction within the actuarial research community. A Poisson mixture model was used to identify major clusters with respect to the frequency of citations by gender across the six journals. The analysis showed that women’s publishing and citation networks are more isolated and have fewer ties than male networks. The paper contributes to the broader literature on the “Matthew effect” in academia. We hope that our study will improve understanding of the gender gap within the actuarial research community and initiate a discussion that will lead to developing strategies for a more diverse, inclusive, and equitable community.

GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK

In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists.

Effect of Image Distortion on Facial Age and Gender Classification Performance of Convolutional Neural Networks

Significant improvement in the task of age group categorization and gender classification of facial images has been achieved using deep convolution neural networks (CNN). In this paper, we study the effect of image distortions such as blur, noise, rotation and occlusion on the performance of a state-of-the-art CNN. We found that the CNN was more sensitive to noise compared to blurring, especially for age estimation. By studying occlusion, we also identified the salient regions of the face. An interesting result is that the upper half of the face is more important for age estimation, while for gender classification it is the lower half. These insights should prove useful for future development of CNN models for facial age and gender classification.

Gender Classification using Fisherface and Support Vector Machine on Face Image

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.

Gender Classification Based on Multiscale Facial Fusion Feature

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

A review of facial gender recognition

Applications such as human---computer interaction, surveillance, biometrics and intelligent marketing would benefit greatly from knowledge of the attributes of the human subjects under scrutiny. The gender of a person is one such significant demographic attribute. This paper provides a review of facial gender recognition in computer vision. It is certainly not a trivial task to identify gender from images of the face. We highlight the challenges involved, which can be divided into human factors and those introduced during the image capture process. A comprehensive survey of facial feature extraction methods for gender recognition studied in the past couple of decades is provided. We appraise the datasets used for evaluation of gender classification performance. Based on the results reported, good performance has been achieved for images captured under controlled environments, but certainly there is still much work that can be done to improve the robustness of gender recognition under real-life environments.

Sexual Dimorphism Analysis and Gender Classification in 3D Human Face

In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method.This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.

Two-Dimensional Heteroscedastic Discriminant Analysis for Facial Gender Classification

In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented, and used for gender classification. In 2DHDA, equal within-class covariance constraint is removed. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, the criterion of 2DHDA, log and rearranging terms are taken, and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly, face images are projected onto the optimal projection matrix, thus the 2DHDA features are extracted. Finally, Nearest Neighbor classifier is selected to perform gender classification. Experimental results show that higher recognition rate is obtained by way of 2DHDA compared with 2DLDA and HDA .

Facial gender classification using shape-from-shading

The aim in this paper is to show how to use the 2.5D facial surface normals (needle-maps) recovered using shape-from-shading (SFS) to perform gender classification. We use principal geodesic analysis (PGA) to model the distribution of facial surface normals which reside on a Remannian manifold. We incorporate PGA into shape-from-shading, and develop a principal geodesic shape-from-shading (PGSFS) method. This method guarantees that the recovered needle-maps exhibit realistic facial shape by satisfying a statistical model. Moreover, because the recovered facial needle-maps satisfy the data-closeness constraint as a hard constraint, they not only encode facial shape but also implicitly encode image intensity. Experiments explore the gender classification performance using the recovered facial needle-maps on two databases (Notre Dame and FERET), and compare the results with those obtained using intensity images. The results demonstrate the feasibility of gender classification using the recovered facial shape information.