Limbic Neurons Shape Sex Recognition and Social Behavior in Sexually Naive Males.

Sexually naive animals have to distinguish between the sexes because they show species-typical interactions with males and females without meaningful prior experience. However, central neural pathways in naive mammals that recognize sex of other individuals remain poorly characterized. We examined the role of the principal component of the bed nucleus of stria terminalis (BNSTpr), a limbic center, in social interactions in mice. We find that activity of aromatase-expressing BNSTpr (AB) neurons appears to encode sex of other animals and subsequent displays of mating in sexually naive males. Silencing these neurons in males eliminates preference for female pheromones and abrogates mating success, whereas activating them even transiently promotes male-male mating. Surprisingly, female AB neurons do not appear to control sex recognition, mating, or maternal aggression. In summary, AB neurons represent sex of other animals and govern ensuing social behaviors in sexually naive males.

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses.

Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

Heterogeneity in the Desire to Undergo Various Gender-Affirming Medical Interventions Among Transgender People in Hong Kong: Findings from a Community-Driven Survey and Implications for the Legal Gender Recognition Debate.

Previous estimates suggest that there are at least nine million transgender people in Asia-Pacific; however, in most countries, legal gender recognition has not been made possible or there are otherwise stringent eligibility criteria. The obligation of having undergone gender-affirming medical interventions as a basis for such recognition is being hotly debated. However, there has been little empirical evidence on the desire to undergo various gender-affirming medical interventions among transgender people. This study fills the research gap by studying Hong Kong, where a transgender person must produce medical evidence for "complete" sex reassignment surgery in order to change the sex entry on their identity card. A community-driven survey of 234 transgender people found that only 13.0% of the participants who were assigned male at birth could fit such a requirement. Strikingly, because none of the participants assigned female at birth had undergone construction of a penis or some form of a penis, all of them would be excluded from legal gender recognition. Financial reasons and reservations about surgical risks and/or techniques were the most commonly cited reasons for not undertaking the medical interventions. The findings suggest that an overwhelming majority of transgender people in Hong Kong are excluded from legal gender recognition, which fundamentally affects their civil, political, economic, social, and cultural rights. More generally, this study shows heterogeneity among transgender people in the desire for different gender-affirming medical interventions, and thus argues that the legal gender recognition debate needs to consider their concerns and self-determination.

Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot.

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.

The implications of the gender-based prohibitions relating to human germline genome editing in the Human Fertilisation and Embryology Act.

RESEARCH QUESTION: What are the implications of the gender-based prohibitions relating to human germline genome editing (hGGE) in the Human Fertilisation and Embryology (HFE) Act 1990, as amended in 2008? DESIGN: A three-phase primary research design consisting of a mixed-methods online public survey of 521 UK citizens aged 16-82 years, 13 semi-structured interviews with experts and professionals involved in the future of hGGE, and structured interviews with 21 people affected by genetic conditions. The research was conducted between March 2018 and October 2019. RESULTS: Gender-based prohibitions in the HFE Act weaken its intent to prevent germline cells that have been altered from resulting in a pregnancy and the possible birth of people with edited genomes. This weakness could become increasingly problematic as genome editing technologies develop and social advances seek to eradicate gendered expectations and gendered binaries. CONCLUSION: The HFE Act should be amended to avoid gender-based discrimination and the potential gender-based prohibitions have to circumvent germline genome editing being used before the technology is considered safe enough to prevent disease.

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms.

Speech signals are being used as a primary input source in human-computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.

Trans Parenthood and the Meaning of 'Mother', 'Father' and 'Parent'-R (McConnell and YY) v Registrar General for England and Wales [2020] EWCA Civ 559.

In R (McConnell and YY) v Registrar General for England and Wales [2020] EWCA Civ 559, the Court of Appeal held the Registrar General was correct to register a trans man, who had given birth after the issuing of his gender recognition certificate, as 'mother' on his son's birth certificate. In their judgement, the court rejected the appellants' contention that the Gender Recognition Act 2004 should be construed to allow registration as either 'father' or 'parent'. The court further held that the interference with the appellants' Article 8 rights which resulted from the registration as 'mother' was proportionate and justified.

Using a Rotating 3D LiDAR on a Mobile Robot for Estimation of Person's Body Angle and Gender.

We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people's features (states or attributes), which are important in human-robot interaction. In particular, we tested the estimation of the person's body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements.

Fine-Grained Face Annotation Using Deep Multi-Task CNN.

We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN's parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations' sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Transgender Sterilisation Requirements in Europe.

The possibility of individuals procreating post-transition has long stalked debates on transgender rights. In 1972, Sweden became the first European jurisdiction to formally acknowledge preferred gender. Under the original Swedish law, applicants for gender recognition were explicitly required to prove an incapacity to reproduce-either through natural infertility or through a positive act of sterilisation. Across the Council of Europe, 20 countries continue to enforce a sterilisation requirement. When considering reforms to their current gender recognition rules as recently as 2015, the Polish executive and the Finnish legislature both rejected proposals to remove mandatory infertility provisions. This article critiques the rationales for transgender sterilisation in Europe. It places transgender reproduction, and non-traditional procreation, in the wider context of European equality and family law. Adopting a highly inter-disciplinary framework, the article explores legal, social, medical, and moral arguments in favour of sterilisation, and exposes the weak intellectual and evidential basis for the current national laws. The article ultimately proposes a new departure for Europe's attitude towards transgender parenting, and argues that sterilisation should not be a pre-condition for legal recognition.

Health, disability and quality of life among trans people in Sweden-a web-based survey.

BACKGROUND: Swedish research concerning the general health of trans people is scarce. Despite the diversity of the group, most Swedish research has focused on gender dysphoric people seeking medical help for their gender incongruence, or on outcomes after medical gender-confirming interventions. This paper examines self-rated health, self-reported disability and quality of life among a diverse group of trans people including trans feminine, trans masculine, and gender nonbinary people (identifying with a gender in between male of female, or identify with neither of these genders) as well as people self-identifying as transvestites. METHODS: Participants were self-selected anonymously to a web-based survey conducted in 2014. Univariable and multivariable regression analyses were performed. Three backward selection regression models were conducted in order to identify significant variables for the outcomes self-rated health, self-reported disability and quality of life. RESULTS: Study participants included 796 individuals, between 15 and 94 years of age who live in Sweden. Respondents represented a heterogeneous group with regards to trans experience, with the majority being gender nonbinary (44 %), followed by trans masculine (24 %), trans feminine (19 %) and transvestites (14 %). A fifth of the respondents reported poor self-rated health, 53 % reported a disability and 44 % reported quality of life scores below the median cut-off value of 6 (out of 10). Nonbinary gender identity (adjusted Odds Ratio (aOR) = 2.19; 95 % CI: 1.24, 3.84), negative health care experiences (aOR = 1.92; 95 % CI: 1.26, 2.91) and not accessing legal gender recognition (aOR = 3.06; 95 % CI: 1.64, 5.72) were significant predictors for self-rated health. Being gender nonbinary (aOR = 2.18; 95 % CI: 1.35, 3.54) and history of negative health care experiences (aOR = 2.33; 95 % CI: 1.54, 3.52) were, in addition, associated with self-reported disability. Lastly, not accessing legal gender recognition (aOR = 0.32; 95 % CI: 0.17, 0.61) and history of negative health care experiences (aOR = 0.56; 95 % CI: 0.36, 0.88) were associated with lower quality of life. CONCLUSIONS: The results of this study demonstrate that the general health of trans respondents is related to vulnerabilities that are unique for trans people in addition to other well-known health determinants.

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras.

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

YY v TURKEY: INFERTILITY AS A PRE-CONDITION FOR GENDER CONFIRMATION SURGERY.

In YY v Turkey, the Second Chamber of the European Court of Human Rights (ECtHR) held that Turkey's refusal, over a period of many years, to authorise gender confirmation surgery because the applicant remained capable of procreating was a violation of the right to private life under Art. 8 of the European Convention on Human Rights. The Second Chamber's judgment acknowledges, and gives practical effect to, the 'physical and moral security' of transgender persons. YY has the potential to revolutionise gender confirming health care in Europe and will hopefully ensure that, where individuals do seek to medically transition, they need only access to treatments that are both necessary and desired. The ECtHR's decision may also impact upon the legal recognition of transgender identities. While not the direct focus of the Second Chamber's assessment, legal gender recognition is a constant theme throughout the judgment, and many of the Court's arguments are equally applicable to legal schemes for acknowledging preferred gender.

A gender recognition method based on EEG microstates.

BACKGROUND: Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning. METHODS: We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification. RESULTS: We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy. CONCLUSION: Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.

Task-related modulation of motor response to emotional bodies: A TMS motor-evoked potential study.

Exposure to emotional body postures during perceptual decision-making tasks has been linked to transient suppression of motor reactivity, supporting the monitoring of emotionally relevant information. However, it remains unclear whether this effect occurs implicitly, i.e., when emotional information is irrelevant to the task. To investigate this issue, we used single-pulse transcranial magnetic stimulation (TMS) to assess motor excitability while healthy participants were asked to categorize pictures of body expressions as emotional or neutral (emotion recognition task) or as belonging to a male or a female actor (gender recognition task) while receiving TMS over the motor cortex at 100 and 125 ms after picture onset. Results demonstrated that motor-evoked potentials (MEPs) were reduced for emotional body postures relative to neutral postures during the emotion recognition task. Conversely, MEPs increased for emotional body postures relative to neutral postures during the gender recognition task. These findings indicate that motor inhibition, contingent upon observing emotional body postures, is selectively associated with actively monitoring emotional features. In contrast, observing emotional body postures prompts motor facilitation when task-relevant features are non-emotional. These findings contribute to embodied cognition models that link emotion perception and action tendencies.

Automatic gender and unilateral load state recognition for biometric purposes.

BACKGROUND: Automatic recognition of a person's gender as well as his or her unilateral load state are issues that are often analyzed and utilized by a wide range of applications. For years, scientists have recognized human gait patterns for purposes connected to medical diagnoses, rehabilitation, sport, or biometrics. OBJECTIVE: The present paper makes use of ground reaction forces (GRF) generated during human gait to recognize gender or the unilateral load state of a walking person as well as the combination of both of those characteristics. METHODS: To solve the above-stated problem parameters calculated on the basis of all GRF components such as mean, variance, standard deviation of data, peak-to-peak amplitude, skewness, kurtosis, and Hurst exponent as well as leading classification algorithms including kNN, artificial neural networks, decision trees, and random forests, were utilized. Data were collected by means of Kistler's force plates during a study carried out at the Bialystok University of Technology on a sample of 214 people with a total of 7,316 recorded gait cycles. RESULTS: The best results were obtained with the use of the kNN classifier which recognized the gender of the participant with an accuracy of 99.37%, the unilateral load state with an accuracy reaching 95.74%, and the combination of those two states with an accuracy of 95.31% which, when compared to results achieved by other authors are some of the most accurate. CONCLUSION: The study has shown that the given set of parameters in combination with the kNN classifying algorithm allows for an effective automatic recognition of a person's gender as well as the presence of an asymmetrical load in the form of a hand-carried briefcase. The presented method can be used as a first stage in biometrics systems.

Twitter-based gender recognition using transformers.

Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.

FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences.

Security threats are always there if the human intruders are not identified and recognized well in time in highly security-sensitive environments like the military, airports, parliament houses, and banks. Fog computing and machine learning algorithms on Gait sequences can prove to be better for restricting intruders promptly. Gait recognition provides the ability to observe an individual unobtrusively, without any direct cooperation or interaction from the people, making it very attractive than other biometric recognition techniques. In this paper, a Fog Computing and Machine Learning Inspired Human Identity and Gender Recognition using Gait Sequences (FCML-Gait) are proposed. Internet of things (IoT) devices and video capturing sensors are used to acquire data. Frames are clustered using the affinity propagation (AP) clustering technique into several clusters, and cluster-based averaged gait image(C-AGI) feature is determined for each cluster. For training and testing of datasets, sparse reconstruction-based metric learning (SRML) and Speeded Up Robust Features (SURF) with support vector machine (SVM) are applied on benchmark gait database ADSC-AWD having 80 subjects of 20 different individuals in the Fog Layer to improve the processing. The performance metrics, for instance, accuracy, precision, recall, F-measure, C-time, and R-time have been measured, and a comparative evaluation of the projected method with the existing SRML technique has been provided in which the proposed FCML-Gait outperforms and attains the highest accuracy of 95.49%.