Gender Recognition with Face Images Based on Partially Connected Neural Evolutionary

In this paper,a new type of neural network model - Partially Connected Neural Evolutionary (PARCONE) was introduced to recognize a face gender. The neural network has a mesh structure in which each neuron didn't connect to all other neurons but maintain a fixed number of connections with other neurons. In training,the evolutionary computation method was used to improve the neural network performance by change the connection neurons and its connection weights. With this new model,no feature extraction is needed and all of the pixels of a sample image can be used as the inputs of the neural network. The gender recognition experiment was made on 490 face images (245 females and 245 males from Color FERET database),which include not only frontal faces but also the faces rotated from-40°-40° in the direction of horizontal. After 300-600 generations' evolution,the gender recognition rate,rejection rate and error rate of the positive examples respectively are 96.2%,1.1%,and 2.7%. Furthermore,a large-scale GPU parallel computing method was used to accelerate neural network training. The experimental results show that the new neural model has a better pattern recognition ability and may be applied to many other pattern recognitions which need a large amount of input information.

Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications(e.g.,sur-veillance and human–computer interaction[HCI]).Images of varying levels of illumination,occlusion,and other factors are captured in uncontrolled environ-ments.Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances,and faces may be covered by a scarf,hijab,or mask due to the COVID-19 pandemic.The periocular region is a reliable source of information because it features rich discriminative biometric features.However,most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environ-ments.Motivated by the superior performance of deep learning,we proposed a new method,PeriGender,inspired by the design principles of the ResNet and DenseNet models,that can classify gender using features from the periocular region.The proposed system utilizes a dense concept in a residual model.Through skip connections,it reuses features on different scales to strengthen dis-criminative features.Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods.It achieved 87.37%,94.90%,94.14%,99.14%,and 95.17%accuracy on the GROUPS,UFPR-Periocular,Ethnic-Ocular,IMP,and UBIPr datasets,respectively,in the open-world(OW)protocol.It further achieved 97.57%and 93.20%accuracy for adult periocular images from the GROUPS dataset in the closed-world(CW)and OW protocols,respectively.The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features,and feminine features can be identified through the eyebrow,upper eyelids,and corners of the eyes.Furthermore,using a whole region without cropping enhances PeriGender’s learning capability,improving its understanding of both eyes’global structure without discontinuity.

Gender Recognition by MULBP

Gender recognition has gained more attention. However, most of the studies have focused on face images acquired under controlled conditions. In this paper, we investigate gender recognition on real-life faces. We proposed a gender recognition scheme, which is composed of four parts: face detection, median filter, feature extraction, and gender classifier. MULBP features are adopted and combined with a SVM classifier for gender recognition. The MULBP feature is robust to noise and illumination variations. In the experiment, we obtain 98.32% using LFW database and 97.30% on Samsung Gender dataset, which shows the superior performance in gender recognition compared with the conventional operators.

Twitter Users' Privacy Concerns: What do Their Accounts' First Names Tell Us?

Purpose： In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements. Design/methodology/approach： We worked with two different data sets to examine whether Twitter users＇ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings. Findings： We found that the inferred gender of Twitter users correlates with the account＇s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user＇s privacy preference. Research limitations： It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A maj or limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space. Practical implications： Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users＇ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users＇ provided first names and their profile descriptions, can be used to infer the users＇ privacy preference.Originality/value： This study explored a new way of inferring Twitter user＇s gender, that is, to recognize the user＇s gender based on the provided first name and the user＇s profile description. The potential of this information for predicting the user＇s privacy preference is explored.

Is It Easy to Recognize Baby's Age and Gender?

Face analysis tasks,e.g.,estimating gender or age from a face image,have been attracting increasing interest in recent years.However,most existing studies focus mainly on analyzing an adult's face and ignore an interesting question:is it easy to estimate gender and age from a baby's face?In this paper,we explore this interesting problem.We first collect a new face image dataset for our research,named BabyFace,which contains 15528 images from 5872 babies younger than two years old.Besides gender,each face image is annotated with age in months from 0 to 24.In addition,we propose new age estimation and gender recognition methods.In particular,based on SSR-Net backbone,we introduce the attention mechanism module to solve the age estimation problem on the BabyFace dataset,named SSR-SE.In the part of gender recognition,inspired by the age estimation method,we also use a two-stream structure,named Two-Steam SE-block with Augment(TSSEAug).We extensively evaluate the performance of the proposed methods against the state-of-the-art methods on BabyFace.Our age estimation model achieves very appealing performance with an estimation error of less than two months.The proposed gender recognition method obtains the best accuracy among all compared methods.To the best of our knowledge,we are the first to study age estimation and gender recognition from a baby's face image,which is complementary to existing adult gender and age estimation methods and can shed some light on exploring baby face analysis.