Personal Image Research Articles

Discriminative feature mining with relation regularization for person re-identification

The appearance attribute and pose are two important and complementary features, so integrating them can effectively alleviate the impact of misalignment and occlusion on re-identification. In this paper, we deeply investigate the inner relation between attribute features and the spatial semantic relation between key-point region features of the pose in a person image and propose a person re-identification method based on discriminative feature mining with relation regularization. Firstly, an attribute relation detector based on nonlinear graph convolution is built on mining the inner correlation between attribute features of a person, providing relational attribute features for more effectively distinguishing persons with a similar appearance. Then, we construct a hierarchical pose pyramid to model the multi-grained semantic features of key-point regions of the pose and propose intra-graph and cross-graph node relation information propagation structures to infer the spatial semantic relation between node features within-graph and between-graph. This module is robust to complex pose changes and can suppress noise background redundancy caused by inaccurate key point detection and occlusion. Finally, a refined feature model is proposed to effectively fuse the global appearance feature with the relational attribute and multi-grained pose features, thus providing a more discriminative fusion feature for person re-identification. Many experiments on three large-scale datasets verify the effectiveness and state-of-the-art performance of the proposed method.

From Iris Image to Embedded Code: System of Methods

Passwords are ubiquitous in today’s world, as are forgetting and stealing them. Biometric signs are harder to steal and impossible to forget. This paper presents a complete system of methods that takes a secret key and the iris image of the owner as input and generates a public key, suitable for storing insecurely. It is impossible to obtain source data (i.e., secret key or biometric traits) from the public key without the iris image of the owner, the irises of other persons will not help. At the same time, when the iris image of the same person is presented the secret key is restored. The system has been tested on several iris image databases from public sources. It allows storing 65 bits of the secret key, with zero possibility to unlock it with the impostor’s iris and 10.4% probability to reject the owner in one attempt.

Sensory modality as a linguistic sign of the ‘divided self’ in John Banville’s novels

As one of the master stylists of our time, John Banville has honed his own unique style of writing. The typical Banville novel is a first-person confessional narrative of an aging male character troubled by his painful memories of failure and loss. In a struggle to cope with their traumatic life experiences, Banville’s protagonists attempt to find answers to haunting existential questions and rediscover their identities in the face of emotional fragmentation. This sense of dislocation and displacement thus emerges as a major theme of Banville’s fiction and his works generally revolve around the internal conflicts of a ‘divided self’. The purpose of this study is to investigate how the language of the novels reflects the inner split of the characters and what linguistic mechanisms Banville exploits to create the ‘divided self’ effect. This article examines a particular linguistic structure used as a pervasive narrative feature: sensory modality. I will more specifically explore sensory modality patterns with co-referential subject and object pronouns (referred here as ‘special effects’) analyzing them in the light of Systemic Functional Grammar as mental transitivity processes and will demonstrate how they constitute a powerful stylistic tool for constructing the image of the divided personality and for conveying self-disunity in retrospective novels.

Lula and His Politics of Cunning: From Metalworker to President of Brazil

Lula and His Politics of Cunning: From Metalworker to President of Brazil

Towards a Controllable and Reversible Privacy Protection System for Facial Images through Enhanced Multi-Factor Modifier Networks

Privacy protection data processing has been critical in recent years when pervasively equipped mobile devices could easily capture high-resolution personal images and videos that may disclose personal information. We propose a new controllable and reversible privacy protection system to address the concern in this work. The proposed scheme can automatically and stably anonymize and de-anonymize face images with one neural network and provide strong security protection with multi-factor identification solutions. Furthermore, users can include other attributes as identification factors, such as passwords and specific facial attributes. Our solution lies in a modified conditional-GAN-based training framework, the Multi-factor Modifier (MfM), to simultaneously accomplish the function of multi-factor facial anonymization and de-anonymization. It can successfully anonymize face images while generating realistic faces satisfying the conditions specified by the multi-factor features, such as gender, hair colors, and facial appearance. Furthermore, MfM can also de-anonymize de-identified faces to their corresponding original ones. One crucial part of our work is design of physically meaningful information-theory-based loss functions, which include mutual information between authentic and de-identification images and mutual information between original and re-identification images. Moreover, extensive experiments and analyses show that, with the correct multi-factor feature information, the MfM can effectively achieve nearly perfect reconstruction and generate high-fidelity and diverse anonymized faces to defend attacks from hackers better than other methods with compatible functionalities. Finally, we justify the advantages of this work through perceptual quality comparison experiments. Our experiments show that the resulting LPIPS (with a value of 0.35), FID (with a value of 28), and SSIM (with a value of 0.95) of MfM demonstrate significantly better de-identification effects than state-of-the-art works. Additionally, the MfM we designed can achieve re-identification, which improves real-world practicability.

Pengaruh Beauty Influencer dan Perceived Price Fairness Terhadap Keputusan Pembelian Produk Skincare

Due to the rapid increase of beauty influencer and global skincare product numbers, local skincare brands need to consider the most appropriate beauty influencer and pricing in order to attract potential consumers. This study aims to analyze the effect of beauty influencers and perceived price fairness on purchasing decisions through brand personality, brand trust and brand image. This research was conducted through an online survey using a voluntary sampling technique to 200 respondents with the criteria; consumers of skin care products aged 20-35 years who have made purchases in the last three months, have social media accounts and have seen beauty influencer reviews. Data were analyzed using Structural Equation Modeling (SEM) Lisrel. The results showed that the effect of beauty influencer and perceived price fairness had an indirect influence on purchasing decisions through intervention variables. Beauty influencers have a significant influence on purchasing decisions through brand personality, brand trust and brand image. Meanwhile, the perceived price fairness has a significant influence on purchasing decisions through brand image. Keywords: beauty influencer, perceived price fairness, purchase decision, skincare

Fame and redemption: On the moral dangers of celebrity apologies

Fame and redemption: On the moral dangers of celebrity apologies

Fragile Watermarks Detecting Forged Images

Technology and digital communications have advanced so that digital photos, videos, or text may be easily manipulated by those not authorized to do so. In addition, the availability of specialized picture editing programs like Photoshop has simplified the process of altering photographs. At first glance, there may seem to be no problem, especially when an image editing method is necessary to delete or add a certain scene that increases the picture’s beauty. But what about personal images or images with copyright? Attempts are constantly made to spoof these images using different approaches. Therefore, measures to reduce the likelihood of counterfeiting in digital and printed forms of media are required. The proposed approach aims to detect a counterfeit in images using a unique generator that conceals the data represented by the embedded watermark utilizing modern visual cryptography and hash algorithms. Image extractions may easily be analyzed for signs of forgery. As a result, our approach will detect and validate phony documents and images.

A novel Multi-scale architecture driven by decoupled semantic attention transfer for person image generation

Person image generation is a challenging task aimed to transfer the person of the source image from a source pose to a target pose while preserving its style. In this paper, we proposed a Generative Adversarial Network based on Decoupled Semantic Attention Transfer (DSAT-GAN), focusing on that local semantic representations of different image styles and contents cannot be accurately decoupled and transferred. This architecture employs a novel Multi-scale Semantic Mapping Generation Network (Ms-SMGN), driven by two network modules with different semantic attention mechanism, aiming to accurately align and transfer the representations of local semantics at different spatial scales. Then, a channel-separated convolution is applied in the encoding networks instead of the traditional channel fully-connected operation, which reduces computational complexity while realizing channel semantic decoupling. Moreover, a Gram matrix-based global style loss is introduced to further enhance the consistency of high-level semantic between generated and target images. Experiments on Market-1501 and DeepFashion datasets show that DSAT-GAN has superior performance compared with other recent baselines. Additionally, this architecture can be extended to the data enhancement scenes to significantly improve the accuracy of person Re-identification.

The Image of Woman in The Novel Wa Nasitu Anni Imro’ah By Ihsan Abdul Quddus and Novel Kartini By Abidah El Khaileqy (Comparative Literature Study)

This study aims to describe the comparison of the image of women in the novel Wa Nasitu Anni Imro'ah by Ihsan Abdul Quddus and the Novel Kartini by Abidah El Khaileqy (Comparative Literature Study). This type of research was qualitative with descriptive method. Qualitative research is research that intends to understand the phenomenon of what is experienced by research subjects such as actors, perceptions, motivations, and actions holistically and by way of description in the form of words and language, in a special natural context. The results of this study present some of the data obtained as research evidence, namely, the female image of the novel Wa Nasitu Anni Imro'ah by Ihsan Abdul Quddus and the Novel Kartini by Abidah El Khaileqy contained in this novel, namely: 1) personal image of women 2) image of women in the family, and 3) the image of women in society. The two novels show that women are capable and can become a highly educated person, have a career and get the same rights as men, without having to be limited by customary rules.

Adaptations of the News Media and Social Media Logics by Indonesian Political Parties’ Leaders and Presidential Candidates after 2004 Indonesian Presidential Election

Adaptations of political actors and organizations in Asian democratic countries to the media and social media logics have been increasingly visible. This article is specified to answer the following questions of how Indonesian political parties’ leaders and presidential candidates after 2004 Indonesian presidential election adapted to the news and social media logics and what drove such adaptations. Focusing on such questions, this article adopts theoretical models of mediatisation of politics formulated by Mazzoleni and Schulz, Strömbäck and Stromback and Van Aelts and extracts in-depth interview data derived from six Indonesian political parties’ leaders, two political consultants and six senior journalists associated with Indonesian media using the qualitative content and thematic analyses. The findings are as follows. These political parties’ leaders and presidential candidates favoured adapting to the news and social media logics when they got to deal with political issues and affairs, formulate political policies and secure their personal images in Indonesian political sphere. Such development was driven by the following factors, including establishing democratic political and media systems, increasing the roles of the media as sources of political information, escalating freedoms of Indonesian media and journalists to manage the media logic. Theoretical implications are chronicled and research recommendations are displayed.

DO-VTON: a details-oriented virtual try-on network

PurposeThe paper aims to transfer the item image of a given clothing product to a corresponding area of the user image. Existing classical methods suffer from unconstrained deformation of clothing and occlusion caused by hair or poses, which leads to loss of details in the try-on results. In this paper, the authors present a details-oriented virtual try-on network (DO-VTON), which allows synthesizing high-fidelity try-on images with preserved characteristics of target clothing.Design/methodology/approachThe proposed try-on network consists of three modules. The fashion parsing module (FPM) is designed to generate the parsing map of a reference person image. The geometric matching module (GMM) warps the input clothing and matches it with the torso area of the reference person guided by the parsing map. The try-on module (TOM) generates the final try-on image. In both FPM and TOM, attention mechanism is introduced to obtain sufficient features, which enhances the performance of characteristics preservation. In GMM, a two-stage coarse-to-fine training strategy with a grid regularization loss (GR loss) is employed to optimize the clothing warping.FindingsIn this paper, the authors propose a three-stage image-based virtual try-on network, DO-VTON, that aims to generate realistic try-on images with extensive characteristics preserved.Research limitations/implicationsThe authors’ proposed algorithm can provide a promising tool for image based virtual try-on.Practical implicationsThe authors’ proposed method is a technology for consumers to purchase favored clothes online and to reduce the return rate in e-commerce.Originality/valueTherefore, the authors’ proposed algorithm can provide a promising tool for image based virtual try-on.

Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique

Face recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies.

Recent progress in person re-ID

行人再识别通过大时空范围内跨摄像机目标行人图像的检索与匹配，可实现人脸等生物特征失效情况下的行人关联，已成为智能视频监控系统的关键环节和支撑技术，并在智慧公安、智慧城市等国民经济建设中发挥了重要作用。近年行人再识别技术吸引了越来越多的关注，并取得了快速发展与进步。本文在对行人再识别技术进行简介的基础上，面向行人再识别的技术发展和落地应用需求与挑战，总结分析遮挡行人再识别、无监督行人再识别、虚拟数据生成、域泛化行人再识别、换装行人再识别、跨模态行人再识别和行人搜索等热点方向的前沿进展，归纳其发展现状和存在问题，最后对行人再识别技术的发展趋势进行展望。希望通过总结和分析，能够为研究人员开展行人再识别相关研究、推动行人再识别技术进步提供参考。;Person re-Identification(person re-ID) technique aims to solve the problem of association and matching of target person images across multiple cameras within a camera network of a surveillance system, especially in the case of face, iris and other biometrics recognition failure under non-cooperative application scenarios, and has become one of the key component and supporting technique for intelligent video surveillance systems and applications in intelligent public security and smart cities. Recently, person re-ID has attracted more and more attention from both academia and industry, and has made rapid development and progress. To meet the requirement of its technical challenges and application needs of person re-ID in practical scenarios, this paper will first give a brief introduction to the development history, commonly used datasets and evaluation metrics. Then, the recent progress in hot research topics of person re-ID is extensively reviewed and analyzed, which includes:occluded person re-ID, unsupervised person re-ID, virtual data generation, domain generalization, clothchanging person re-ID, cross-modal person re-ID and person search. First, to address the problem of impact of possible occlusions on the performance of person re-ID, recent progress in occluded person re-ID is first reviewed, in which the popular datasets for occluded person re-ID are briefly introduced, and the two major categories of occluded person re-ID models are then further reviewed. Second, facing the challenges of low-efficiency and high-cost data annotation and great impact of training data on the performance of person re-ID, unsupervised person re-ID and virtual data generation for person re-ID emerges as two hot topics in person re-ID. The paper elaborates the recent advances of unsupervised person reID, which can be classified into three major categories:pseudo label generation-based models, domain transfer-based models, and other related models, which take into consideration of the extra information like time-stamps, camera labels besides person image. Third, the-state-of-the-art works on virtual data generation for person re-ID are reviewed, with detailed introduction and performance comparisons of major virtual datasets. Fourth, recent researches on domain generalization person re-ID will be reviewed, which are classified into five categories:batch/instance normalization models, domain invariant feature learning models, deep-learning-based explicit image matching models, models based on mixture of experts and meta-learning-based models. Fifth, since most current person re-ID models largely depend on the color appearance of persons' clothes, clothes-changing person re-ID becomes a challenging setting, in which person images can exhibit large intra-class variation and small inter-class variation. Typical cloth-changing person re-ID datasets are introduced and the recent researches will then be reviewed, in which models in the first category explicitly introduces extra cloth-appearance-independent features like contour and face while the second try to decouple the cloth features and person ID features. Sixth, to compensate the drawbacks of conventional person re-ID of visible light/RGB images in natural complex scenes like poor lighting conditions in the night, the-state-of-the-art of cross-modal person re-ID, which aims to resolve the problem through other visible RGB images-excluded heterogeneous data, are reviewed, with a brief introduction of commonly used cross-modal person re-ID datasets first and then four sub-categories models according to the different modalities employed, including:RGB-infrared image person re-ID, RGB image-text person re-ID, RGB image-sketch person re-ID, and RGB-depth image person re-ID, respectively. Seventh, since existing person re-ID benchmarks and methods mainly focus on matching cropped person images between queries and candidates and is different from practical scenarios where the bounding box annotations of persons are often unavailable, person search, which jointly considers person detection and person re-ID in a single framework, becomes a new hot research topic. The typical datasets and recent progress on person search are reviewed. Finally, the existing challenges and development trend of person re-ID techniques are discussed. It is hoped that the summary and analysis can provide reference for relevant researchers to carry out research on person re-ID and promote the progress of person re-ID techniques and applications.

Learning Disentangled Representation Implicitly Via Transformer for Occluded Person Re-Identification

Person re-IDentification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-offi-the-art by large margins for occluded re-ID dataset.

Assessing Conscientiousness and Identify Leadership Quality Using Temporal Sequence Images

Human Facial expressions exhibits the inner personality. Evaluating the inner personality is performed through questionnaires during recruitment process. However, the evaluation through questionnaires performs less due to anxiety, and stress during interview and prediction of leadership quality becomes a challenging problem. To the above problem, Temporal sequence based SENet architecture (TSSA) is proposed for accurate evaluation of personality trait for employing the correct person for leadership position. Moreover, SENet is integration with modern architectures for performance evaluation. In Proposed TSSA, face book facial images of a particular person for a period of one month and face images collect from different social environments and forms the sequential facial image database are analysed for personality trait estimation. Now a days, Facebook plays a vital role, where people express their emotions by posting images and updating their profile pictures. In TSSA method, 50 Facebook temporal sequence of images of person with answered questionaries during the face image collection forms as a Temporal sequence image (TSI) database for prediction of the Big Five personality trait. In order to get precise prediction, we have analysed the face images that were posted in a period of one month and validated the result with the next month face images from face book. Face images for predicting the personality, where asked to fill the Questionnaires through Google Forms increase the accuracy in prediction. The TSSA prediction results are utilized for assessment of a person’s conscientiousness for leadership quality suitability. The study implements Deep Learning algorithm with SENet architecture and compares with traditional algorithms. From the validation results the proposed TSSA method performs 96% of accuracy in conscientiousness prediction.

Health-Related Quality of Life of Prostate Cancer Patients on Radiotherapy Treatment at Cancer Diseases Hospital in Lusaka, Zambia

Prostate Cancer is a major Public Health problem because of its high incidence and mortality. It is feared by men due to negative stigma brought by its diagnosis and its psychological effects, which affect the perception of sexuality and their own personal image and thereby affect the quality of life. The aim of the study was to investigate the health-related quality of life and associated factors of patients receiving radiotherapy treatment for prostate cancer at the Cancer Diseases Hospital in Lusaka, Zambia.

БИЗНЕС-МОДЕЛЬ ОРГАНИЗАЦИИ ГОСТИНИЧНО- РЕСТОРАННЫХ УСЛУГ: ФОРМИРОВАНИЕ КАЧЕСТВЕННЫХ КОМПЕТЕНЦИЙ ПЕРСОНАЛА

When forming a business model for the organization of hotel and restaurant services, the level of quality of staff competencies plays an important role for the successful achievement of strategic development goals and improv- ing a favorable image. At this stage of scientific research, it is important to study in more detail the trends and specifics of building a business model in the field of hotel and restaurant services and the formation of high-quality staff competencies based on marketing the approach. The established business models of the flagships in the tourism industry maintain their successful and recognized professional practices, unique and individual standards of service, their own brand and personal image with confi- dence and tradition. Consumer preferences and habits form a special segment in the tourism industry: regular custom- ers who, from anywhere in the world, cast their vote in favor of recognizing their brand in the field of hotel and restaurant services. The factors of formation of types of individual hotels and inns, for example, Soft Brand hotels, are determined. For example, adapting to market trends – hotels under the Soft brand are flexible and adaptable, which allows them to quickly respond to changing market trends and guest preferences. This flexibility helps them stay relevant and compet- itive in the dynamically developing hotel business. Recognizing the importance of these key growth drivers in the hotel sector under the soft brands is vital to maximize their value. A systematic list of digital technologies that are used to form business models in the field of hotel and restaurant services is presented, which highlights digital technologies that are used to improve the quality of professional competencies of staff. Currently, in the field of hotel and restaurant services, the penetration of innovative and digital technologies and tools into the system of promotion of specific services aimed at the formation of innovative hotel and restaurant services is observed, the formation of innovative business models that complement classic business models with creative and fashionable ideas and proposals for improving the hospitality industry is recorded.

Exploring Kernel-Based Texture Transfer for Pose-Guided Person Image Generation

Pose-guided person image generation that aims to transfer the pose of a given person to a target pose has recently received lots of research attention. Due to the spatial misalignment and occlusions of different local body parts by pose variations, this task is still challenging especially in maintaining high-fidelity textures and body structures in generated images. Besides, most works also suffer from the limited number of texture styles in the given person datasets, restricting the diversity of generated persons' appearances. To solve these problems, we design a Kernel-based Texture-Fusion Joint Refinement Network (TFJR-Net) to jointly refine the structure and texture information of generated images. First, we leverage a bone-map representation to guide the generation of human parsing maps, which has more structure priors and richer context information than traditional key-point maps, thus reduce the uncertainty of generated body structures. Next, a Texture-Kernel Injection Normalization module (TKIN) is proposed to inject the per-region texture-kernel into the corresponding semantic region from the human parsing map, which decouples the texture and shape information, and also preserves fine-grained features for complex textures. Furthermore, we are the first to introduce external texture patterns outside of the dataset in human semantic regions such as the upper clothes. We fuse the two texture domains in a shared texture space through our designed texture-fusion TKIN modules. Extensive experiments are conducted on the Deepfashion dataset, with the DTD dataset as an external texture source. The experimental results demonstrate the superiority of our proposed method in generating persons of better textures and structures than state-of-the-art works, and also show the generalization ability of our proposed method to absorb diversified external textures for generating person images. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pilgrim00/TKIN</uri> .

OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.