Verification of Models of Personal Perception of Faces by Closed-Eye Classifier Using Histogram Correlation

2015 ◽  
Author(s):  
J. Romero ◽  
L. Diago ◽  
J. Shinoda ◽  
I. Hagiwara
Keyword(s):  
Computational Models ◽  
State Of The Art ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Facial Appearance ◽  
Social Decisions ◽  
Verification Of Models ◽  
The Relationship

People rapidly form impressions from facial appearance, and these impressions affect social decisions. Data-driven, computational models are the best available tools for identifying the source of such impressions. However, the computational models cannot be accepted unless they have passed the tests of validation to ascertain their credibility. In this paper, the condition of the eyes of the person is used to validate the fuzzy rules extracted from the computational models. A simple and effective classifier is proposed to evaluate the closeness of the eyes during the evaluation of a small database of portraits. The experimental results show that closed-eyes can be detected only after the proposed shift of the normalized histogram is applied. Although it is very simple, the proposed classifier can achieve better accuracy than other state of the art classifiers. The relationship between the closeness of the eyes and the evaluation of the subjects is also analyzed.

scholarly journals Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 969
Author(s):  
JongGeun Oh ◽  
Min-Cheol Hong
Keyword(s):  
Light Source ◽  
State Of The Art ◽  
Contrast Ratio ◽  
Mapping Function ◽  
Experimental Results ◽  
Nonlinear Mapping ◽  
Low Light ◽  
Reconstruction Process ◽  
Image Rendering ◽  
The Relationship

This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods.

scholarly journals State of the Art Study of Influence of Bed Roughness and Alluvial Cover on Bedrock Channels and Comparisons of Existing Models with Laboratory Scale Experiments

2020 ◽  
Author(s):  
Jagriti Mishra ◽  
Takuya Inoue
Keyword(s):  
Mathematical Models ◽  
State Of The Art ◽  
Theoretical Models ◽  
Sediment Supply ◽  
Laboratory Scale ◽  
Experimental Results ◽  
Bed Topography ◽  
Bed Roughness ◽  
The Relationship ◽  
Bedrock Channels

Abstract. Several studies have implied towards the importance of bed roughness on alluvial cover, besides, several mathematical models have also been introduced to mimic the effect bed roughness may project on alluvial cover. Here, we provide a state of the art review of research exploring the relationship between alluvial cover, sediment supply and bed topography, thereby, describing various mathematical models used to analyse deposition of alluvium. In the interest of analysing the efficiency of various available mathematical models, we performed laboratory-scale experiments and compared the results with various models. Our experiments show that alluvial cover is not merely governed by increasing sediment supply, and, bed topography is an important controlling factor of alluvial cover. Testing experimental results with various theoretical models suggest a fit of certain models for a particular bed topography and inefficiency in predicting higher roughness topography. Three models efficiently predict the experimental observations, albeit their limitations which we discuss here in detail.

scholarly journals exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)

2019 ◽  
Vol 9 (19) ◽  
pp. 4062 ◽  
Author(s):  
Heejung Jwa ◽  
Dongsuk Oh ◽  
Kinam Park ◽  
Jang Kang ◽  
Hueiseok Lim
Keyword(s):  
State Of The Art ◽  
The Body ◽  
Data Driven ◽  
Detection Methods ◽  
News Story ◽  
Fake News ◽  
Improve Performance ◽  
News Stories ◽  
Detection Model ◽  
The Relationship

News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.

scholarly journals A Data-Driven Emotion Model for English Learners Based on Machine Learning

2021 ◽  
Vol 16 (08) ◽  
pp. 34
Author(s):  
Zhao Zheng ◽  
Kew Si Na
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Facial Expression ◽  
English Learners ◽  
Experimental Results ◽  
Data Driven ◽  
Proposed Model ◽  
Research Findings ◽  
Emotion Model ◽  
The Relationship

Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning.

scholarly journals Learning a Genetic Measure for Kinship Verification Using Facial Images

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Lu Kou ◽  
Xiuzhuang Zhou ◽  
Min Xu ◽  
Yuanyuan Shang
Keyword(s):  
Similarity Measure ◽  
Genetic Similarity ◽  
State Of The Art ◽  
Experimental Results ◽  
Facial Appearance ◽  
Similarity Learning ◽  
Kinship Verification ◽  
Practical Applications ◽  
In The Wild ◽  
Facial Images

Motivated by the key observation that children generally resemble their parents more than other persons with respect to facial appearance, distance metric (similarity) learning has been the dominant choice for state-of-the-art kinship verification via facial images in the wild. Most existing learning-based approaches to kinship verification, however, are focused on learning a genetic similarity measure in a batch learning manner, leading to less scalability for practical applications with ever-growing amount of data. To address this, we propose a new kinship verification approach by learning a sparse similarity measure in an online fashion. Experimental results on the kinship datasets show that our approach is highly competitive to the state-of-the-art alternatives in terms of verification accuracy, yet it is superior in terms of scalability for practical applications.

scholarly journals Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

2021 ◽  
Author(s):  
Xiaobin Liu ◽  
Shiliang Zhang
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Previous Method ◽  
Experimental Results ◽  
Clear Margin ◽  
Training Images ◽  
Teacher Networks ◽  
Consistency Constraint ◽  
Similarity Relationships ◽  
The Relationship

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

Parasites and promiscuity: Acute disease salience leads to more restricted sexual attitudes

2021 ◽  
pp. 026540752110309
Author(s):  
James B. Moran ◽  
Nicholas Kerry ◽  
Jin X. Goh ◽  
Damian R. Murray
Keyword(s):  
Social Behavior ◽  
Sexual Attitudes ◽  
Experimental Manipulation ◽  
Sexual Relationships ◽  
Experimental Results ◽  
Acute Disease ◽  
Short Term ◽  
Attitudes And Behaviors ◽  
The Relationship ◽  
Sexual Promiscuity

How does disease threat influence sexual attitudes and behaviors? Although research on the influence of disease threat on social behavior has grown considerably, the relationship between perceived disease threat and sexual attitudes remains unclear. The current preregistered study (analyzed N = 510), investigated how experimental reminders of disease threat influence attitudes and anticipated future behaviors pertaining to short-term sexual relationships, using an ecologically valid disease prime. The central preregistered prediction was that experimental manipulation of disease threat would lead to less favorable attitudes and inclinations toward sexual promiscuity. Results were consistent with this preregistered prediction, relative to both a neutral control condition and a non-disease threat condition. These experimental results were buttressed by the finding that dispositional variation in worry about disease threat predicted less favorable attitudes and inclinations toward short-term sexual relationships. This study represents the first preregistered investigation of the implications of acute disease threat for sexual attitudes.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

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