scholarly journals A Novel Multi-Level Pyramid Co-Variance Operators for Estimation of Personality Traits and Job Screening Scores

2021 ◽  
Vol 38 (3) ◽  
pp. 539-546
Author(s):  
Hichem Telli ◽  
Salim Sbaa ◽  
Salah Eddine Bekhouche ◽  
Fadi Dornaika ◽  
Abdelmalik Taleb-Ahmed ◽  
...  
Keyword(s):  
Personality Traits ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Directional Pattern ◽  
Image Features ◽  
Big Five Personality ◽  
Sequence Information ◽  
Face Features ◽  
The Face ◽  
Multi Level

Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new framework for estimating the Big-Five personality traits and job candidate screening variable from video sequences. The framework consists of two parts: (1) the use of Pyramid Multi-level (PML) to extract raw facial textures at different scales and levels; (2) the extension of the Covariance Descriptor (COV) to fuse different local texture features of the face image such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). Therefore, the COV descriptor uses the textures of PML face parts to generate rich low-level face features that are encoded using concatenation of all PML blocks in a feature vector. Finally, the entire video sequence is represented by aggregating these frame vectors and extracting the most relevant features. The exploratory results on the ChaLearn LAP APA2016 dataset compare well with state-of-the-art methods including deep learning-based methods.

Factors influencing young children’s ability to discriminate unfamiliar faces by age

2000 ◽  
Vol 24 (4) ◽  
pp. 480-491 ◽  
Author(s):  
P. A. George ◽  
G. J. Hole ◽  
M. Scaife
Keyword(s):  
Discrimination Performance ◽  
Image Features ◽  
Age Discrimination ◽  
Face Features ◽  
Factors Influencing ◽  
The Face ◽  
Gaussian Blurring ◽  
High Degree ◽  
Unfamiliar Faces

Three experiments examined young children’s ability to discriminate between pairs of unfamiliar faces which differed in age. Apre-test found that 99% of 6-year-olds, but only 36%of 4-year-olds, could reliably decide which of two faces was the oldest. Experiment 1 attempted to identify the nature of the information used for age-processing faces. Face-pairs were presented in four different versions: Original (unmodified image); Features-only (containing only the internal face features); Skin-blur (in which the skin regions of the face were subjected to Gaussian blurring); or Overall-blur (in which the entire image was blurred). The last three versions selectively reduced specific cues to age. No significant differences in age-discrimination performance were found between these different versions, suggesting that, as with adults, children are capable of adaptively using a variety of cues in order to discriminate between faces on the basis of age. Experiments 2 and 3 investigated in more detail a phenomenon suggested by Experiment 1: That children found it easier to discriminate between faces by age that were similar in age to themselves, than between adult faces. The results suggest that children as young as 6 years can use age to discriminate between faces of all ages with a relatively high degree of accuracy, but experience most difficulty with adult faces.

scholarly journals Face Spoofing Detection using Enhanced Local Binary Pattern

2019 ◽  
Vol 9 (2) ◽  
pp. 3365-3371
Keyword(s):  
Neural Network ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Substantial Improvement ◽  
Novel Approach ◽  
The Face ◽  
The Difference ◽  
Lbp Descriptor ◽  
Face Spoofing ◽  
Made In

Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniquesfordetection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep features are more robust in comparison to the former one. In this paper, a proper countermeasure in opposite to attacks that are on face spoofing are relied on CNN i.e. Convolutional Neural Network. In this novel approach, deep texture features from images are extracted by integrating the modified version of LBP descriptor (Gene LBP net) to a CNN. Experimental results are obtained on NUAA spoofing database which defines that these deep neural network surpass most of the state-of-the-art techniques, showing good outcomes in context to finding out the criminal attacks

scholarly journals Detail 3D Face Reconstruction Based on 3DMM and Displacement Map

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianping Li ◽  
Hongxin Xu ◽  
Hua Zhang ◽  
Honglin Wan
Keyword(s):  
Texture Features ◽  
Facial Feature ◽  
Reconstruction Method ◽  
3D Face Reconstruction ◽  
Face Surface ◽  
Face Model ◽  
Face Reconstruction ◽  
Adversarial Network ◽  
Face Features ◽  
The Face

How to accurately reconstruct the 3D model human face is a challenge issue in the computer vision. Due to the complexity of face reconstruction and diversity of face features, most existing methods are aimed at reconstructing a smooth face model with ignoring face details. In this paper a novel deep learning-based face reconstruction method is proposed. It contains two modules: initial face reconstruction and face details synthesis. In the initial face reconstruction module, a neural network is used to detect the facial feature points and the angle of the pose face, and 3D Morphable Model (3DMM) is used to reconstruct the rough shape of the face model. In the face detail synthesis module, Conditional Generation Adversarial Network (CGAN) is used to synthesize the displacement map. The map provides texture features to render to the face surface reconstruction, so as to reflect the face details. Our proposal is evaluated by Facescape dataset in experiments and achieved better performance than other current methods.

scholarly journals Estimating the Associations between Big Five Personality Traits, Testosterone, and Cortisol

2020 ◽  
Author(s):  
Zachary Sundin ◽  
William J. Chopik ◽  
Keith M Welker ◽  
Esra Ascigil ◽  
Cassandra M Brandes ◽  
...  
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Theoretical Models ◽  
Big Five Personality Traits ◽  
Hormonal Contraceptives ◽  
Big Five Personality ◽  
Biological Models ◽  
Behavioral Indicators ◽  
Multi Level ◽  
Meta Analyses

**Objective**: Hormones are often conceptualized as biological markers of individual differences and have been associated with a variety of behavioral indicators and characteristics, such as mating behavior or acquiring and maintaining dominance. However, before researchers create strong theoretical models for how hormones modulate individual and social behavior, information on how hormones are associated with dominant models of personality are needed. Although there have been some studies attempting to quantify the associations between personality traits, testosterone, and cortisol, there are many inconsistencies across these studies. **Methods**: In this registered report, we examined associations between testosterone, cortisol, and Big Five personality traits. We aggregated 25 separate samples to yield a single sample of 3,964 (50.3% women; 27.7% of women were on hormonal contraceptives). Participants completed measures of personality and provided saliva samples for testosterone and cortisol assays.**Results**: The results from multi-level models and meta-analyses revealed mostly weak, non-significant associations between testosterone or cortisol and personality traits. The few significant effects were still very small in magnitude (e.g. testosterone and conscientiousness: r = -0.05). A series of moderation tests revealed that hormone-personality associations were mostly similar in men and women, those using hormonal contraceptives or not, and regardless of the interaction between testosterone and cortisol (i.e., a variant of the dual-hormone hypothesis). **Conclusions**: Altogether, we did not detect many robust associations between Big Five personality traits and testosterone or cortisol. The findings are discussed in the context of biological models of personality and the utility of examining heterogeneity in hormone-personality associations.

A Face Recognition Algorithm Based on Intermediate Layers Connected by the CNN

2021 ◽  
Author(s):  
Yuxiang Long
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Network Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Face Features ◽  
Intermediate Layers ◽  
Training Samples ◽  
The Face

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper.

The Face Detection Research Based on Multi-Scale and Rectangle Feature

2012 ◽  
Vol 198-199 ◽  
pp. 1383-1388
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Feature Extraction ◽  
Image Features ◽  
Face Image ◽  
High Dimensions ◽  
Feature Extraction Method ◽  
Face Features ◽  
Haar Feature ◽  
The Face ◽  
Speed Up ◽  
Feature Expression

When extracting the face image features based on pixel distribution in face image, there always exist large amount of calculation and high dimensions of feature sector generated after feature extraction. This paper puts forward a feature extraction method based on prior knowledge of face and Haar feature. Firstly, the Haar feature expressions of face images are classified and the face features are decomposed into edge feature, line feature and center-surround feature, which are further concluded into the expressions of two rectangles, three rectangles and four rectangles. In addition, each rectangle varies in size. However, for face image combination, there exist too much redundancy and large calculation amount in this kind of expression. In order to solve the problem of large amount of calculation, the integral image is adopted to speed up the rectangle feature calculation. In addition, the thought based on classified trainer is adopted to reduce the redundancy expression. The results show that using face image of Haar feature expression can improve the speed and efficiency of recognition.

scholarly journals An Integrated Technique for Face Sketch Recognition Using DCNN

2019 ◽  
Vol 8 (10) ◽  
pp. 3133-3140
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Image Features ◽  
Local Descriptor ◽  
Multi Scale ◽  
Face Sketch Recognition ◽  
Tchebichef Moments ◽  
The Face ◽  
Integrated Technique

Face Recognition (FR) is considered as one of the chief use in the investigation of criminals. In the majority of the cases, information about the criminal is not available. In such situations, sketch artist draw the sketch of the guess with the oral explanation provided by the eyewitness. These sketches can then be matched manually against mug shot photos. This process is time-consuming. Hence there require a method that efficiently goes with composite sketches to the gallery of mug shot databases. Thus the proposed system uses a scheme for matching composite sketch and photo images, photo image features are extracted and fused to train the system. Composite Sketch feature is matched with face photo images. Feature extraction (FE) is done using Multi-Scale Local Binary Patterns (MLBP) Tchebichef Moments and Multiscale Circular Weber Local Descriptor (MCWLD), Principal Component Analysis (PCA) is used for fusion of extracted features, DCNN used as a classifier to recognize the face. The experiments are conducted using PRIP-HDC dataset and the proposed system gives good accuracy in face recognition.

The Relationship Between Narcissism and Personality Traits of the Five-Factor-Model in Adolescents and Young Adults

2018 ◽  
Vol 46 (6) ◽  
pp. 516-522 ◽  
Author(s):  
Marc Allroggen ◽  
Peter Rehmann ◽  
Eva Schürch ◽  
Carolyn C. Morf ◽  
Michael Kölch
Keyword(s):  
Young Adults ◽  
Personality Traits ◽  
Five Factor Model ◽  
Self Report ◽  
Adolescents And Young Adults ◽  
Personality Factors ◽  
Big Five Personality ◽  
Grandiose Narcissism ◽  
Vulnerable Narcissism ◽  
The Relationship

Abstract.Narcissism is seen as a multidimensional construct that consists of two manifestations: grandiose and vulnerable narcissism. In order to define these two manifestations, their relationship to personality factors has increasingly become of interest. However, so far no studies have considered the relationship between different phenotypes of narcissism and personality factors in adolescents. Method: In a cross-sectional study, we examine a group of adolescents (n = 98; average age 16.77 years; 23.5 % female) with regard to the relationship between Big Five personality factors and pathological narcissism using self-report instruments. This group is compared to a group of young adults (n = 38; average age 19.69 years; 25.6 % female). Results: Grandiose narcissism is primarily related to low Agreeableness and Extraversion, vulnerable narcissism to Neuroticism. We do not find differences between adolescents and young adults concerning the relationship between grandiose and vulnerable narcissism and personality traits. Discussion: Vulnerable and grandiose narcissism can be well differentiated in adolescents, and the pattern does not show substantial differences compared to young adults.

Intelligence and Personality

2020 ◽  
Vol 41 (3) ◽  
pp. 124-132
Author(s):  
Marc-André Bédard ◽  
Yann Le Corff
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Real Life ◽  
Big Five Personality Traits ◽  
Original Study ◽  
Life Assessment ◽  
Big Five Personality ◽  
Verbal Intelligence ◽  
Nonverbal Intelligence ◽  
French Canadian

Abstract. This replication and extension of DeYoung, Quilty, Peterson, and Gray’s (2014) study aimed to assess the unique variance of each of the 10 aspects of the Big Five personality traits ( DeYoung, Quilty, & Peterson, 2007 ) associated with intelligence and its dimensions. Personality aspects and intelligence were assessed in a sample of French-Canadian adults from real-life assessment settings ( n = 213). Results showed that the Intellect aspect was independently associated with g, verbal, and nonverbal intelligence while its counterpart Openness was independently related to verbal intelligence only, thus replicating the results of the original study. Independent associations were also found between Withdrawal, Industriousness and Assertiveness aspects and verbal intelligence, as well as between Withdrawal and Politeness aspects and nonverbal intelligence. Possible explanations for these associations are discussed.

Personality of Clown Doctors

2016 ◽  
Vol 37 (1) ◽  
pp. 49-55 ◽  
Author(s):  
Alberto Dionigi
Keyword(s):  
General Population ◽  
Personality Traits ◽  
Big Five ◽  
Normal Population ◽  
Big Five Personality Traits ◽  
Published Data ◽  
Big Five Personality

Abstract. In recent years, both professional and volunteer clowns have become familiar in health settings. The clown represents a peculiar humorist’s character, strictly associated with the performer’s own personality. In this study, the Big Five personality traits (BFI) of 155 Italian clown doctors (130 volunteers and 25 professionals) were compared to published data for the normal population. This study highlighted specific differences between clown doctors and the general population: Clown doctors showed higher agreeableness, conscientiousness, openness, and extraversion, as well as lower neuroticism compared to other people. Moreover, specific differences emerged comparing volunteers and professionals: Professional clowns showed significantly lower in agreeableness compared to their unpaid colleagues. The results are also discussed with reference to previous studies conducted on groups of humorists. Clowns’ personalities showed some peculiarities that can help to explain the facility for their performances in the health setting and that are different than those of other groups of humorists.

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