Face Identification Research Articles

Developing a Grounded Theory Model of Effective Factors and Consequences of Gender-Affirming for Transgender People and Those with Undifferentiated Gender in Iran

Background: Transgender people and those who have an undifferentiated gender identity face various personal and social challenges due to their dissatisfaction with the gender role and the desire to behave differently from the gender role. Thus, focusing on this group of people is one of the issues in health and psychology. To this end, the present study aimed to explore more deeply the challenges and problems faced by this group of people. Methods: This qualitative study was conducted based on the grounded theory proposed by Corbin and Strauss on 15 members of social networks and the Association of People with Gender Dissatisfaction. The participants were selected through purposive sampling from among those with undifferentiated gender identities from childhood to adulthood. The data were collected via individual in-depth interviews with the participants. The collected data were analyzed using the systematic grounded theory approach to extract related categories and codes and develop a conceptual model. Results: The analysis of the data revealed 54 themes that were grouped into 9 subcategories and 5 main categories. Gender affirming was extracted as the core phenomenon. The causal factors included the feeling of living in another person’s body and problems in social relationships. Contextual factors were divided into 4 subcategories including family challenges, positive and negative public reactions, peer relationship problems, and economic challenges. The intervening factors were school problems and the role of media. In addition, the consequences were the positive and negative experiences of life in another body. Finally, the extracted strategies were support and individual coping strategies. Conclusion: Social factors and family and school play an important role in aggravating the problems of people with gender dissatisfaction. Focusing on empowering these people can help them adopt effective coping strategies. Besides, given the lack of recognition for people with gender dissatisfaction, raising public awareness of the challenges faced by these people is particularly important.

MASKED AND UNMASKED FACE RECOGNITION ON UNCONSTRAINED FACIAL IMAGES USING HAND-CRAFTED METHODS

In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep learning systems. A low-cost system is intended to be constructed for recognizing masked faces and compares its performance to that of face recognition systems that do not use masks. The proposed method fuses hand-crafted methods using feature-level fusion strategy. This study compares the performance of masked and unmasked face recognition systems. The experiments are undertaken on two publicly accessible datasets for masked face recognition: Masked Labeled Faces in the Wild (MLFW) and Cross-Age Labeled Faces in the Wild (CALFW). The best accuracy is achieved as 94.8% on MLFW dataset. The rest of the results on different train and test sets from CALFW and MLFW datasets are encouraging compared to the state-of-the-art models.

The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks.

Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here, we asked whether the "other-race effect" (ORE)-the human advantage for identifying own- vs. other-race faces-exacerbates morph attack susceptibility for humans. We also asked whether face-identification performance in a deep convolutional neural network (DCNN) is affected by the race of morphed faces. Caucasian (CA) and East-Asian (EA) participants performed a face-identity matching task on pairs of CA and EA face images in two conditions. In the morph condition, different-identity pairs consisted of an image of identity "A" and a 50/50 morph between images of identity "A" and "B". In the baseline condition, morphs of different identities never appeared. As expected, morphs were identified mistakenly more often than original face images. Of primary interest, morph identification was substantially worse for cross-race faces than for own-race faces. Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs. Notably, the deep network proved substantially more accurate than humans in both cases. The results point to the possibility that DCNNs might be useful for improving face identification accuracy when morphed faces are presented. They also indicate the significance of the race of a face in morph attack susceptibility in applied settings.

“Who the Hell Are You?”

Tom Stoppard is an outstanding playwright, embellishing his plays with scientific and philosophical approaches and presenting complicated, mysterious plots to the reader. He bases the plot of his postmodern play, Hapgood (1988), on quantum theory and draws an analogy between Werner Heisenberg’s principle of indeterminism, termed “The Uncertainty Principle”, and international espionage. Thus, he constructs the complicated relationship between the particle and the whole throughout the play to depict the relativistic and deceptive relationship between sex and gender. Focusing on the title character of the play, the present study proposes that the play represents the fluid and indeterminate nature of gender and identity within the context of espionage and indeterminism. Hapgood, who is a mother, a lover and a successful master agent in a man-dominated British secret service, navigates between “masculinity” and “femininity” during a day. Moreover, she metamorphoses into her twin, who is entirely different from her, except for their identical faces, to entrap the mole in the office. The study consults Judith Butler’s theory of gender performativity and fluidity to indicate the unpredictable and uncertain nature of ‘gender identity’, which formulates itself through performances in social relations and trespasses the heterosexual matrix. It juxtaposes the Butlerian approach with the Uncertainty Principle in quantum theory pertaining to the confusing relationship between gender, the body and identity. The Butlerian analysis of the play reveals that particularly the modern way of life makes women transgress the Cartesian dichotomies of sex/gender, masculine/feminine and man/woman through the fluidity of their roles even during a single day and makes it difficult to find out who is really who.

Ensemble coding of facial identity is robust, but may not contribute to face learning

Ensemble coding – the rapid extraction of a perceptual average – has been proposed as a potential mechanism underlying face learning. We tested this proposal across five pre-registered experiments in which four ambient images of an identity were presented in the study phase. In Experiments 1 and 2a-c, participants were asked whether a test image was in the study array; these experiments examined the robustness of ensemble coding. Experiment 1 replicated ensemble coding in an online sample; participants recognize images from the study array and the average of those images. Experiments 2a-c provide evidence that ensemble coding meets several criteria of a possible learning mechanism: It is robust to changes in head orientation (± 60o), survives a short (30s) delay, and persists when images of two identities are interleaved during the study phase. Experiment 3 examined whether ensemble coding is sufficient for face learning (i.e., facilitates recognition of novel images of a target identity). Each study array comprised four ambient images (variability + average), a single image, or an average of four images (average only). Participants were asked whether a novel test image showed the identity from a study array. Performance was best in the four-image condition, with no difference between the single-image and average-only conditions. We conclude that ensemble coding of facial identity is robust but that the perceptual average per se is not sufficient for face learning.

Joint Segmentation and Identification Feature Learning for Occlusion Face Recognition.

The existing occlusion face recognition algorithms almost tend to pay more attention to the visible facial components. However, these models are limited because they heavily rely on existing face segmentation approaches to locate occlusions, which is extremely sensitive to the performance of mask learning. To tackle this issue, we propose a joint segmentation and identification feature learning framework for end-to-end occlusion face recognition. More particularly, unlike employing an external face segmentation model to locate the occlusion, we design an occlusion prediction module supervised by known mask labels to be aware of the mask. It shares underlying convolutional feature maps with the identification network and can be collaboratively optimized with each other. Furthermore, we propose a novel channel refinement network to cast the predicted single-channel occlusion mask into a multi-channel mask matrix with each channel owing a distinct mask map. Occlusion-free feature maps are then generated by projecting multi-channel mask probability maps onto original feature maps. Thus, it can suppress the representation of occlusion elements in both the spatial and channel dimensions under the guidance of the mask matrix. Moreover, in order to avoid misleading aggressively predicted mask maps and meanwhile actively exploit usable occlusion-robust features, we aggregate the original and occlusion-free feature maps to distill the final candidate embeddings by our proposed feature purification module. Lastly, to alleviate the scarcity of real-world occlusion face recognition datasets, we build large-scale synthetic occlusion face datasets, totaling up to 980193 face images of 10574 subjects for the training dataset and 36721 face images of 6817 subjects for the testing dataset, respectively. Extensive experimental results on the synthetic and real-world occlusion face datasets show that our approach significantly outperforms the state-of-the-art in both 1:1 face verification and 1:N face identification.

Adversarial Secret-Identity Generation Model for Face Anonymization in the Internet of Vehicles

With the rapid development of the Internet of Vehicles, the systematical cameras in vehicles can easily collect mass personal face data, which is very private on the Internet. This article focuses on solving the face protection problem caused by the Internet of Vehicles. Previous studies have proposed various face protection models based on obfuscation, neural networks, or adversarial techniques to prevent the disclosure of identity information. Even with existing face anonymization algorithms, there are several issues, including weak information protection ability, not maintaining the naturalness of a protected face, and not being suitable for various emerging face recognition systems. We propose an adversarial secret-identity generation model to protect personal identities from these problems. Concretely, our model's identity encryption generative adversarial module can generate brand new faces, which ensures naturalness while making other face identification models unable to recognize the identity. In addition, our PFS module extracts the identity set that is least relevant to the inputs and most conducive to model convergence from target data and promotes the generation of excellent faces from the perspective of data. Finally, the protected identities generated by our black-box face deidentification model can resist recognizing various advanced face identification approaches. Extensive experiments prove our model can achieve a 96.5% protection success rate, achieving competitive performance with 98.1% R1-T, 99.2% R5-T, 98.5% R1-U, and 97.1% R5-U against ArcFace and beating out various superior face anonymization algorithms. Meanwhile, we introduce a model compression algorithm to reduce the network scale of this model, demonstrating the scalability of various intelligent vehicles.

Masked Face Identification and Tracking Using Deep Learning: A Review

Masked Face Identification and Tracking Using Deep Learning: A Review

Anonymization of face images with Contrastive Learning

Abstract Photos or videos taken by individuals often carry sensitive details such as facial identities, which has led to an escalating societal interest in privacy protection measures. We suggest an improved face identity transformer that offers password-protected anonymization and de-anonymization of photo-realistic facial images in visual data. Our face identity transformer is designed to (1) erase facial identity information after anonymization, (2) restore the original face when a correct password is provided and (3) generate an incorrect but realistic face when given an incorrect password. The processes of image anonymization and de-anonymization are facilitated through a password scheme, a multi-task learning objective and generative adversarial networks comprising InfoGAN and contrastive learning. In-depth experiments indicate that our methodology can execute anonymization and de-anonymization based on password conditions whilst reducing training time and enhancing image quality compared to existing anonymization procedures. Additionally, it maintains a recognition rate as low as 4.8% for anonymized images without sacrificing the face detection rate of the original method.

Artificial intelligence-based camel face identification system for sustainable livestock farming

Artificial intelligence-based camel face identification system for sustainable livestock farming

RBGAN: Realistic-generation and balanced-utility GAN for face de-identification

The rise of computer vision technologies and surveillance devices has led to numerous studies on vision, but it also raises privacy concerns, especially regarding facial identity information. Protecting the privacy of face identity information has prompted the development of face de-identification, which faces two main challenges. The first challenge is the realism of the generated images, while the second challenge is finding the proper balance between privacy protection and preserving data utility. To address these challenges, we propose a face de-identification model called Realistic-Generation and Balanced-Utility GAN (RBGAN). RBGAN includes a disentangled module and a symmetric-consistency-guided generation module, which aim to preserve multiple attributes while generating realistic de-identified images. We design an attribute-specialized encoder to obtain the disentangled representation of identity and attribute to balance privacy protection and data utility preservation. To generate realistic de-identified images, we incorporate symmetric consistency and face reconstruction into the face de-identification process using the Generative Adversarial Network (GAN). The disentangled module can extract richer representation, and the symmetric-consistency-guided generation module can construct more facial details. As a result, our proposed RBGAN can generate de-identified face images that closely resemble real faces and are difficult to detect by visual systems. Experiments conducted on benchmark face datasets demonstrate the effectiveness of our model in generating realistic images while preserving multiple attributes.

Fine alignment, flexible fusion: A novel framework of multi-model score fusion in face identification

Multi-model score fusion was considered a bottleneck problem in forensic face identification. While the score distribution of different face models varies greatly, the existing score processing methods cannot achieve accurate alignment. This paper proposed a score fusion framework named fine alignment and flexible fusion framework (FAFF). In FAFF, we took score-based likelihood ratios as the reference values to align the similarity scores generated by different face models. First, we set up a unified calibration test workflow based on the forensic likelihood ratio test. Then, 3 LLR anchor-based methods (LLRBA1, LLRBA2, and LLRBA3) and LLR curve-based methods (LLRBC) were proposed. Finally, we conducted fusion experiments on four face models (VGGface, Facenet, Arcface, and SFace). The experimental results show that on the CelebA dataset, compared with the existing MOEBA and PAN methods, LLRBC increased the TPR@ 10−7 FPR by 175.4 % and 162.9 %, and LLRBA increased by 55.6 % and 48.5 %.

Sistem Buka-Tutup Sebuah Pintu Rumah Menggunakan Face Recognition Berbasis Mikrokontroler

Internet of Things technology is now widely used in all aspects of life including the application of smart homes. An automation technology is used to facilitate access at home, as well as access to the entrance of the house that uses facial image recognition (face detection). Human facial image recognition is one of the important technologies that continues to develop in the field of computer vision with its application in biometric recognition systems. One of them is by using the facial image recognition method, the Haar Cascade Classifier Algorithm. Therefore, a house door open-close system with facial recognition is designed using the ESP32-Cam module as a camera sensor. Face images were taken using the ESP32-Cam camera and processed using the ESP32 Microcontroller through the Arduino IDE program. Recognition results will be forwarded from the microcontroller to the PIR sensor, DC motor, buzzer, LED and website. The test result of this system is that if the detected face ID matches the data base, the PIR sensor will be active to detect the movement of objects in front of the entrance. When motion is detected, the buzzer as an alarm will be ON and the door will open and close automatically. If Face ID is not detected then the door will not open.

Too Muslim to Be Homosexual or Too Homosexual to Be Muslim: Belonging Experiences of British Homosexual Muslims

Abstract This article shows that British homosexual Muslims face rejection and identity conflict between their homosexuality and their Muslimness. The opposition between Islam and homosexuality has created a feeling of exclusion, illustrating the assumed incompatibility between being Muslim and being homosexual. Homosexual Muslims face religiously motivated homophobia rooted in the heteronormative precepts of Islam. In parallel, they face Islamophobic attitudes in which Islam is now used as a form of civilisational opposition to the British values of tolerance and inclusion and the wider homosexual community see it as a threat to their very existence. Nevertheless, the results show that the hostility of Muslims toward homosexuality is evolving, and the heteronormative discourses are now coexisting with more neutral and even homo-friendly approaches. A new bicultural belonging among homosexual Muslims is being constructed to address individual strategies of managing both identities and is fostering new interpretations of acceptance of different sexualities within Islam.

E-passport security systems and attack implications

Recent technological advances aim to recognise user data using biometrics such as the face, fingerprint, hand veins and iris. Currently, face prints are widely used to verify user data in e-passports. As a result, institutions face substantial difficulties in maintaining an appropriate level of security. Human error can introduce flaws that undermine security mechanisms. One potential solution to this problem is to install a facial recognition security system. Both hardware and software components make up this system, with the hardware being a camera and the software comprising face detection and identification algorithms.
 
 The purpose of this essay is to provide a thorough understanding of the Face Recognition Security System, including its application and deployment. Furthermore, the essay investigates the various weaknesses and methods of attack that could be used to target the system. The purpose of addressing these factors is to improve the effectiveness and robustness of system security, notably e-passport security.

Queues, crowds, and angry mobs: Face identification under distraction in a virtual airport.

In visual environments, selective attention must be employed to focus on task-relevant stimuli. A key question here concerns the extent to which other stimuli within the visual field influence target processing. In this study, we ask whether face identity matching is subject to similar effects from irrelevant stimuli in the visual field, specifically task-irrelevant people. Although most previous studies rely on highly controlled face and body stimuli presented in isolation, here we use a more realistic environment. Participants take the role of passport officers and must match a person's face to their photo-ID, while other people appear in the background, waiting to be processed. Presenting an interactive virtual environment on screen (Experiments 1 and 2) or in immersive VR (Experiment 3), we generally found no evidence for distraction from background people on face-matching accuracy. However, when immersed in VR, an angry crowd in the background delayed matching speed while not affecting accuracy. We discuss the theoretical implications of these results and their potential importance in practical settings.

Supporting transracially adopted children in white K-12 schools: Strategies for empowerment and inclusion

The transracial adoption of children into white communities is a growing phenomenon. Unfortunately, transracially adopted children are more likely to face identity issues, mental health concerns, and racism in white K-12 schools due to their lack of access to understanding, support, and resources. In order for transracially adopted children to thrive academically and emotionally, it is essential that educators become aware of the unique challenges transracial families face. In this paper, we will examine the challenges transracially adopted children face in white K-12 schools and provide strategies for educators to create welcoming learning communities that empower transracially adopted children and their transracial families.

A Hybrid Stacked CNN Model with Weighted Average Ensembling for Effective Face Recognition

The discipline of computer vision has given a lot of attention to facial recognition. Automated face recognition is extensively employed in various practical scenarios, including systems for streamlining immigration checkpoints, intelligent monitoring of visual data, and authentication of personal identity. Depending on the situation, it may be divided into the two separate duties of facial verification and face identification. This study proposed a hybrid stacked CNN model for face recognition system. The models have mastered the art of making their own inferences. The models are then combined to predict a class value using a cutting-edge method called weighted average ensembling. A more accurate estimate should be produced by the new assembly process. Pre-trained CNN models are used in our proposed method: AlexNet, Resnet50V2, and VGG-19, Yolov5, VGG-16 and ResNet50. When applied to Tufts dataset images, our suggested model successfully achieved 98.05% accuracy. We have also used the Discrete Wavelet Transform (DWT) method for denoising, SegNet for Image segmentation for better performance of the model proposed.

Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module

This research paper describes the conceptualization, deployment, and application of a stress and emotion detection system (SEDS) that utilizes emerging technologies including machine learning, the Internet of Medical Things (IoMT), and fuzzy logic. Particle Photon microcontrollers were used to collect and analyze data from non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and input signals generated by the machine learning-based facial identification sub-module, which detects an individual's emotional states (happy, sad/upset, angry/irritable, or nervous/scared). The acquired parameters were stored and made readily accessible using IoT cloud services and dedicated mobile phone applications. The emotional condition of an individual was assessed by the utilization of the Personal Image Classifier tool within the MIT App Inventor, which involved the analysis of facial expressions. Furthermore, the SEDS was integrated with a module for mental health risk assessment which employs fuzzy logic to categorize the user's stress-related psychological health threat level as very low, low, moderate, high, or extremely high based on the data collected. The customized smartphone application provided users specific recommendations for effectively managing their mental health, based on stress level assessments. When the SEDS determined that the level of mental health risk posed by stress was high, it automatically generated a referral notification and transmitted it via text message to the mental health care professional, facilitating the provision of appropriate psychological counseling. Based on the collected data, the stress and emotion detection system produced results that were comparable to those from the DASS21 stress scale. The system demonstrated an improved accuracy of 90% in a test involving thirty individuals who volunteered to participate. The machine learning-based emotion detection system achieved a classification accuracy of 86.67% in correctly detecting happy, neutral, sad, angry, or nervous feelings through the analysis of facial expressions. This research is designed to provide mental health care professionals such as guidance counselors, psychologists, and psychiatrists with the resources essential to facilitate the evaluation and treatment of mental health issues. It also aims to raise people's understanding and detection of psychological conditions through enhanced awareness initiatives.

Learning a High Fidelity Identity Representation for Face Frontalization

This paper considers the problem of face frontalization in the wild, which transforms a face image with profile views into a frontal face. Face frontalization provides an effective solution to the face recognition problem in uncontrolled scenes. However, the existing methods either focus on deep learning techniques as an end-to-end framework or combine other explicit facial prior estimation tasks, such as 3D representation, optical flow estimation and so on, where computation is highly redundant and facial identity cannot be well represented. In this paper, we focus on how to maximise the potential of the model for identity learning and representation, and propose an accurate and lightweight face frontalization approach, named identity-preserving model (IPM). IPM has a well-designed encoder-decoder architecture which restores input face to a frontal counterpart. The encoder is constructed to extract representation from the input face, where a contrastive loss function is applied that encourages representations to form compact clusters, while preserving their relationships across the corpora. Then a cross-domain rectification module is proposed to eliminate the representation differences between the recognition and reconstruction domains, thus improving the accuracy of the reconstructed face. Extensive experiments on benchmark datasets show that the proposed IPM approach not only outperforms the state-of-the-art on public datasets but also can cope with images in the uncontrolled scenes.