Person Matching Research Articles

2021 Census England And Wales: Developing Record Speed Linkage Methods to Produce Outputs in a Year

Introduction2021 will herald the next census in England and Wales. The Office for National Statistics (ONS) have a goal of publishing outputs within one year, 4 months earlier than in 2011. Since we produce estimates rather than counts, the linkage of the 2021 Census to the Census Coverage Survey which comprises ~710,000 person and ~370,000 household records, has to be carried out in record time (eight weeks) whilst maintaining incredibly high accuracy (less than 0.1% false positives and 0.25% false negatives).
 Objectives and ApproachOur approach is to utilise the ONS Distributed Access Platform to write automated matching algorithms that are both efficient and accurate. These methods use parallelisation to speed things up, active machine learning to iteratively improve our parameters, and associative matching to squeeze every last match out automatically without impairing the accuracy.
 As in 2011, we will be using clerical matchers to resolve cases that cannot be matched automatically. Speeding up the clerical matching process is imperative. We have therefore developed a pre-search algorithm that takes the hard work out of clerical matching by replacing clerical searching (here’s a record can you find a match?) with clerical resolution (here are two or more records, do they match?).
 ResultsAs a result of our improvements we estimate that we have increased our automatic matching rates from 70% to 91% for person matching, and from 60% to 95% for household matching, without loss of accuracy. However, the biggest gains in terms of speed are delivered by our pre-search algorithm which, at the current iteration, is limiting false negatives to ~0.13% according to the 2011 gold standard.
 ConclusionWe estimate that overall our improvements will mean that in 2021 we will need less than half the clerical resource that was required in 2011 and will meet our eight-week deadline.

Joint Person Objectness and Repulsion for Person Search.

Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its neighbors, the gallery images having a higher similarity with the neighbors of probe should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.

The effects of the visual presentation of an Influencer's Extroversion on perceived credibility and purchase intentions—moderated by personality matching with the audience

Influencers are ordinary individuals who have amassed large followings by demonstrating expertise in various niches on social media sites. We aim to propose (1) the visual presentation of an Influencer's extroversion as an antecedent to source credibility and purchase intentions and (2) personality matching in terms of extroversion between an Influencer and their audience as a moderator of such relationships. We chose the source credibility model and the similarity-attraction model as our theoretical frameworks. In our controlled online experiment (n = 165), the profile of a brand ambassador of a fashion brand was curated to create two levels of extroversion (i.e. low vs. high extroversion). Participants' self-reported extroversion levels were counterbalanced in these two conditions to create a personality match with the Influencer, entitled “extroversion congruence.” Our results suggest that the visual presentation of an Influencer's extroversion increases the perceived credibility of the Influencer and subsequent purchase intentions. Additionally, our findings show that these relationships were asymmetrically moderated by extroversion congruence: the positive effects of extroversion increase in the case of high extroversion among both the Influencer and their audience but decrease in the case of low extroversion.

Salient points driven pedestrian group retrieval with fine-grained representation

People often take part in various social activities in the form of groups in public area. As the primary constituent units of crowd, groups retrieval has become one of the urgent issues for the security departments. In this paper, collection of stable individuals with some social relationship, called group, is selected as the research object, and a novel task of pedestrian group retrieval is introduced. Different from the individual person matching, groups often show high aggregation due to their inherent characteristics, occlusions in group individuals therefore are more serious. As a result, the performance of individual person based detection and matching will be affected. Meanwhile, group matching also needs to address the problems like variations in the shape or configuration. Therefore, we suggest that the group entirety may be disassembled into fine-grained representation and then design a salient points driven framework for pedestrian group retrieval. The work focuses on the problems of overall appearance characteristics extraction of a deformable pedestrian collection and matching of groups at varying scales. Experiments on Pedestrian-Groups2 dataset and Road Group dataset demonstrate the effectiveness of our proposed framework for Pedestrian Group retrieval.

The effects of trait-based personalization in social media advertising

Advertisements in social media can be tailored to psychographic profiles based on consumers' digital-trace data. An experiment (N = 936) investigated the conditions under which such trait-based personalization of social media messages is more effective than non-personalized messages and which personality traits and user characteristics are most suitable for this purpose. In a 9 × 2 between-subjects design, the persuasive appeal of a Facebook ad and the advertised product (phone vs. soda) were varied. Participants' ‘Big Five’ personality traits, need for cognition, and susceptibility to persuasive strategies were assessed in a questionnaire. Results showed limited effects of personality matching. Matched messages led to increased intentions to engage with the post when they addressed specific persuasive susceptibilities (particularly toward authority influence). However, there were no consistent effects on consumers' attitudes toward the advertised products. Findings are discussed regarding the ongoing debate on microtargeting in social media.

Does buyer-seller personality match enhance impulsive buying? A green marketing context

ABSTRACT This study investigates the effect of buyer-seller personality similarity on consumers’ impulsive buying. Based on interpersonal similarity theory, the results revealed that buyer-seller similarity in agreeableness and openness increased impulsive buying of agreeable and open-minded buyers with a stronger effect of agreeable buyer-seller dyads. While neurotic buyer-seller dyads showed a significant negative effect on impulsive buying of neurotic buyers. Interestingly, conscientious and extraverted buyer-seller dyads failed to show a significant connection with the impulsive buying behavior. Thus, this study proposes that buyers exhibit different impulsive buying behavior when they interact with corresponding sellers due to their personality matching (mismatching).

Person re-identification based on multi-level feature complementarity of cross-attention with part metric learning

Person re-identification is an image retrieval task, and its task is to perform a person matching in different cameras by a given person target. This research has been noticed and studied by more and more people. However, pose changes and occlusions often occur during a person walking. Especially in the most related methods, local features are not used to simply and effectively solve the problems of occlusion and pose changes. Moreover, the metric loss functions only consider the image-level case, and it cannot adjust the distance between local features well. To tackle the above problems, a novel person re-identification scheme is proposed. Through experiments, we found that we paid more attention to different parts of a person when we look at him from a horizontal or vertical perspective respectively. First, in order to solve the problem of occlusion and pose changes, we propose a Cross Attention Module (CAM). It enables the network to generate a cross attention map and improve the accuracy of person re-identification via the enhancement of the most significant local features of persons. The horizontal and vertical attention vectors of the feature maps are extracted and a cross attention map is generated, and the local key features are enhanced by this attention map. Second, in order to solve the problem of the lack of expression ability of the single-level feature maps, we propose a Multi-Level Feature Complementation Module (MLFCM). In this module, the missing information of high-level features is complemented by low-level features via short skip. Feature selection is also performed among deep features maps. The purpose of this module is to get the feature maps with complete information. Further, this module solves the problem of missing contour features in high-level semantic features. Third, in order to solve the problem that the current metric loss function cannot adjust the distance between local features, we propose Part Triple Loss Function (PTLF). It can reduce both within-class and increase between-class distance of the person parts. Experimental results show that our model achieves high values on Rank-k and mAP on Market-1501, Duke-MTMC and CUHK03-NP.

A Perfect Match? A Scoping Review of the Influence of Personality Matching on Adult Mentoring Relationships-Implications for Academic Medicine.

Mentoring is a widely regarded faculty development strategy in academic medicine. However, the lack of understanding about mentoring relationship dynamics limits effective recruitment, implementation, and evaluation. Despite decades of publications describing adult mentoring initiatives, few studies examine personality influence in mentoring relationships. This scoping review examined the extent, range, and nature of the research on personality matching in mentoring relationships, and identified research gaps in the literature. Scoping review methodology guided a search of six databases representing higher education, health sciences education, and professional contexts where mentoring is used. Consistent with the inclusive approach of a scoping review, authors included academic papers and other article types. The scoping review yielded 39 articles. Literature mostly originated in the United States, publication sources represented multiple disciplines, and the context for the majority of articles was the workplace. The most common publication type was a research report. Although all articles addressed personality or mentoring, only three articles examined personality matching and its contribution to the mentoring relationship. Finally, although the Big Five personality traits were cited in multiple studies, other personality frameworks were used. Academic medicine expends resources developing and supporting mentoring programs but there remains limited understanding of how best to identify and match mentors and protégés. Further understanding of the role of joint and unique personality traits in academic medicine mentoring relationships seems necessary, if the field continues to invest, time, money, and resources for mentoring programs.

Personality test social application system construction - the influence of community platform on personality trait extroversion, real and virtual interpersonal relationship

The rapid change of the times. A smartphone is a part of socialization. Social is a product of the collective society. Different from the social model of the past. The popularity of social software in recent years has also brought about tremendous changes. A social application such as Facebook, Twitter, Whatsapp, etc. All bring a new definition of social style to human society. Since different social software has the same mode of making friends, the face of friends is in appearance or interest. Do we think that dating mode can make changes? There are differences in ’people’. Differences create different individuality, personalities, and traits. Therefore, we use personality traits as the theme to design a mobile-oriented mobile application that allows users to complete the personality test. The topic, whether or not it does not meet its personality traits, has a standard existence. We use the user personality test to test the user’s nine personality scale and can chat and interact with other users through personality matching. Different from general social software, this paper will explore the application value of social software user personality traits, and will have the opportunity to change the interaction mode of today’s social software with different dating experiences, so that the social experience generated by users can be further improved.

LONG WAVE INFRARED IMAGE COLORIZATION FOR PERSON RE-IDENTIFICATION

Abstract. Person re-identification (ReID) in color and thermal images require matching of the object color and its temperature. While thermal cameras increase the performance of ReID systems during the night-time, identification of corresponding features in the visible and the long-wave infrared range is challenging. The biggest challenge arises from the multimodal relationship between an object’s color and its temperature. Modern ReID methods provide state-of-the-art results in person matching in the visible range. Hence, it is possible to perform multimodal matching by translation of a thermal probe image to the color domain. After that, the synthetic color probe image is matched with images from the real color gallery set. This paper is focused on the development of the ThermalReID multispectral person ReID framework. The framework performs matching in two steps. Firstly, it colorizes the input thermal probe image using a Generative Adversarial Network (GAN). Secondly, it matches images in the color domain using color histograms and MSCR features. We evaluate the ThermalReID framework using RegDB and ThermalWorld datasets. The results of the evaluation are twofold. Firstly, the developed GAN performs realistic colorization of thermal images. Secondly, the ThermalReID framework provides matching of persons in color and thermal images that compete with and surpass the state-of-the-art. The developed ThermalReID framework can be used in video surveillance systems for effective person ReID during the nighttime.

Person Re-Identification With Joint Verification and Identification of Identity-Attribute Labels

One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a supervised multi-task learning framework which considers attribute label information with joint identification-verification network to simultaneously learn an attribute-semantic and identity-discriminative feature representation. Specifically, this framework adopts the part-based deep neural network and learn three different tasks simultaneously: person identification, person verifications and attribute identification, so as to discover and capture concurrently complementary discriminative information about a person image from global and local image features and mid-level attribute features in one deep neural network. With the multi-task learning architecture, we obtain a discriminative model that reaches a synergy in distinguishing different person images, as manifested with the competitive accuracy on three person ReID datasets: Market1501, DukeMTMC-reID and VIPeR.

Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification

Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.

Appraisal of mentorship in cardiothoracic surgery training

ObjectivesAlthough the importance of mentorship in training the next generation of cardiothoracic surgeons is widely recognized, the current state of mentorship remains undefined. MethodsTrainee responses to questions in the 2017 In-Training Examination regarding aspects of mentorship were analyzed. Response rate was 78% (288/370). Mentor-related and trainee-related characteristics were assessed. ResultsThe majority (84%) of residents had mentors, with a high impact on specialty choice (80%), and 91% of respondents viewed mentorship as critical to success. Nearly half (42%) had program-assigned mentors; 53% found them as productive, and 13% reported more consistent/frequent meetings than personally selected mentors, with 22% reporting less ideal personality match compared with personally selected mentors. Among residents with mentors, 36% lacked mentorship in work-life balance, 23% lacked mentorship in job assistance, and 22% lacked mentorship in career advice. Junior residents more often valued mentors as role models, whereas mentors chosen by senior residents were more impactful in technical training, job counseling, and societal involvement. Compared with men, women more often valued mentors as role models and assisting in networking. Men reported their mentors were more impactful in teaching technical skills and clinical ability than women. ConclusionsThe majority of current cardiothoracic surgery trainees had mentorship; however, gaps remain: Many residents lacked career path guidance, assistance obtaining a job, and advice regarding life-work balance. The role of mentorship varied with program type, seniority, and gender, emphasizing the need to tailor mentorship to the individual and changing needs of the resident.

Automated Processing of Electronic Data for Disease Surveillance

Automated Processing of Electronic Data for Disease Surveillance

Multi-Channel Pyramid Person Matching Network for Person Re-Identification

In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification. In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations. These correspondence representations are fused to perform the re-identification task. Further, the proposed framework is optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate.

Normalized distance aggregation of discriminative features for person reidentification

We propose an effective person reidentification method based on normalized distance aggregation of discriminative features. Our framework is built on the integration of three high-performance discriminative feature extraction models, including local maximal occurrence (LOMO), feature fusion net (FFN), and a concatenation of LOMO and FFN called LOMO–FFN, through two fast and discriminant metric learning models, i.e., cross-view quadratic discriminant analysis (XQDA) and large-scale similarity learning (LSSL). More specifically, we first represent all the cross-view person images using LOMO, FFN, and LOMO–FFN, respectively, and then apply each extracted feature representation to train XQDA and LSSL, respectively, to obtain the optimized individual cross-view distance metric. Finally, the cross-view person matching is computed as the sum of the optimized individual cross-view distance metric through the min–max normalization. Experimental results have shown the effectiveness of the proposed algorithm on three challenging datasets (VIPeR, PRID450s, and CUHK01).

Occupational health care return-to-work practices for workers with job burnout

Background: Occupational health care supports return to work in cases of burnout; however, there is little research on return-to-work practices.Aim: To describe occupational health care return-to-work practices for workers with burnout and to identify potential for the development of the practices.Methods: Open-ended interviews and essays were used to collect data from 25 occupational health care professionals. A qualitative content analysis method was used.Results: Occupational health care was involved in the return-to-work support in the off-work, work re-entry and maintenance phases during the return-to-work process. However, occupational health care had no influence in the advancement phase. The key return-to-work actions were: (i) defining burnout, (ii) supporting disengagement from work, (iii) supporting recovery, (iv) determining the return-to-work goal, (v) supporting re-engagement with work, (vi) monitoring the job–person match, (vii) re-evaluating the return-to-work goal, (viii) supporting the maintenance of the achieved return-to-work goal, and, where appropriate, (ix) supporting an alternative return-to-work goal. There were varied return-to-work practices among the occupational health care centers evaluated.Conclusions: The occupational health care return-to-work practices for workers with burnout are described with recommendations to further develop common practice guidelines.

Factors Influencing Employee’s Turnover and Their Effects in the Malaysian Private Sector

The word “employee turnover is a crucial metric that is frequently integral to organizations’ personnel arranging and policy. The main rationale of this study is to discover out the legitimate reasons behind employees turnover and its consequences on the productivity of Malaysian manufacturing industries. Based on the indication of the coefficient shows that, the inverse relationship between the turnover intension, carrier promotion, price, personality and job match. On the off chance that the satisfaction level of utilizes in regards to their occupation is elevated, the turnover aim will be stumpy. More over if the level of carrier promotion, price, personality and job match is low; the widrawal intension will be higher. The paper recommend that management ought to utilize both sorts of compensation (Financial and non-Financial), appealing package, convince allowance, group insurance, medical allowance, benevolent fund, house rent, gratuity, and pension in financial remuneration and also match a job with the person caring out the job. The limitations of the study is that respondents are selected only faculty members of private business institutions on convenience premise, speculation for the entire populace is not flawlessly fulfilled although random sampling is applied.

Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning

Person Re-Identification (Re-Id) is among the main constituents of an automated visual surveillance system. It aims at finding out true matches of a given query person from a large repository of non-overlapping camera images/videos. In this paper, we have proposed an efficient Re-Id approach that is based on a highly discriminative hybrid person representation which combines the low-level hand-crafted appearance based features together with the mid-level attributes and semantic based deep features. The low-level hand crafted features are extracted by using hierarchical Gaussian and local histogram distributions in different color spaces. These features incorporate discriminative texture, shape and color information which is invariant to distractors, e.g., variations in pose, viewpoint and illumination, and so on. The mid-level attribute based deep features are extracted to incorporate contextual- and semantic-based information. The feature space is optimized and self-learned using cross-view quadratic discriminant analysis and multiple metric learning, with the aim to reduce the intra-class differences and increase the inter-class variations for robust person matching. The proposed framework is evaluated on publicly available small scale (VIPeR, PRID450s, and GRID) and large scale (CUHK01, Market1501, and DukeMTMC-ReID) person Re-Id datasets. The experimental results show that the hybrid hand-crafted and deep features outperformed the existing state-of-the-art in approaches in the unsupervised paradigm.

Does it matter who teaches you? A study on the relevance of matching students' and teachers' personalities

The purpose of this paper is to identify whether student-teacher personality (mis)match has an impact on student's understanding of the subject matter and learning experience. We begin by hypothesizing that it does matter who teaches you and, based on our personal teaching experience to start with, assume that when students' and teachers' personalities match, students learn more effectively and, therefore, have a better learning experience. We empirically test our assumption among 260 undergraduate business students of 16 different nationalities and 27 teachers in one of the business schools in Europe. We first determine the students' and the teachers' personality type as well as their nationality and then ask students to self-report on their understanding of 27 subjects in their curriculum. Based on descriptive statistics, results show that a (mis)match between the teacher's and the student's personality has no positive or negative impact on student's learning outcome. In other words, it does not matter who teaches you. Our contribution lies in the contrasting results to most empirical evidence on this topic but also a different student cohort in our study. This paper offers possible insight into why we obtained such results, discusses limitations and areas of further research.