Personality Analysis Through Handwriting

This work discusses about computer aidedGraphology i.e. Personality analysis based on handwritten text,in which instead of a Graphologist, a system will be trained toperform the analysis without much human intervention. A realworld dataset of handwritten text samples will be maintained andbased on few features like Margins, Baseline, Size, Zones etc.depicted in each of the samples and extracted through Imageprocessing techniques and , an approximate analysis of thewriter’s personality trait will be done. These traits will then bemapped to the existing personality theories and finally apersonality type, temperament and a detailed report will be givenfor the handwritten sample, as the result of analysis

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels)

The Application of Graphology and Enneagram Techniques in Determining Personality Type Based on Handwriting Features

This research was conducted with the aim of developing previous studies that have successfully applied the science of graphology to analyze digital handwriting and characteristics of his personality through shape based feature extraction, which in the present study will be applied one method of psychological tests commonly used by psychologists to recognize human\u27s personality that is Enneagram. The Enneagram method in principle will classify the personality traits of a person into nine types through a series of questions, which then calculated the amount of the overall weight of the answer. Thickness is what will provide direction personality type, which will then be matched with the personality type of the result of the graphology analysis of the handwriting. Personality type of handwritten analysis results is processed based on the personality traits that are the result of the identification of a combination of four dominant form of handwriting through the software output of previous studies, that Slant (tilt writing), Size (font size), Baseline, and Breaks (respite each word). From the results of this research can be found there is a correlation between personality analysis based on the psychology science to the graphology science, which results matching personality types by 81.6% of 49 respondents data who successfully tested

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

“Nonsense Rides Piggyback on Sensible Things”: The Past, Present, and Future of Graphology

“Nonsense rides piggyback on sensible things”, declares professional sceptic and questioned-document analyst Joe Nickell concerning graphology. This chapter examines graphology’s enduring allure and reach, despite its controversies, and considers its relationship with other types of handwriting analysis. It first asks: is it possible to metaphorically “dissect” the page of handwritten texts, to scrutinize writing as a “medical paratext” rich in information about the writer’s state of health? It then interrogates the nature of the connection between physical and mental states and handwriting. It demonstrates how academics are going “back to basics” with their enquiries into individual difference and handwriting features, and how digital methodologies are contributing to this. Thus, this chapter is an updated study of graphology, providing a wider understanding of the concept of the paratext by considering the information captured in handwriting in the context of a digital age

Chapter 9 “Nonsense Rides Piggyback on Sensible Things”

“Nonsense rides piggyback on sensible things”, declares professional sceptic and questioned-document analyst Joe Nickell concerning graphology. This chapter examines graphology’s enduring allure and reach, despite its controversies, and considers its relationship with other types of handwriting analysis. It first asks: is it possible to metaphorically “dissect” the page of handwritten texts, to scrutinize writing as a “medical paratext” rich in information about the writer’s state of health? It then interrogates the nature of the connection between physical and mental states and handwriting. It demonstrates how academics are going “back to basics” with their enquiries into individual difference and handwriting features, and how digital methodologies are contributing to this. Thus, this chapter is an updated study of graphology, providing a wider understanding of the concept of the paratext by considering the information captured in handwriting in the context of a digital age

SMART Recruiters' Helper

In today's world, recruiting the right employees becomes one of the most important success factors for almost all the businesses. However, most of the employers stated that inaccurate judgments about the candidates during the interview session has brought a huge negative impact to the companies. Hence, a system named SMART Recruiters’ Helper that helps to analyze the candidates' personalities based on their handwriting has been developed. To explain in more details, the system allows the recruiters to answer the evaluation questions in the system and the system will analyze the candidates’ personalities based on these answers. This project will then be started with the objectives of identifying the criteria used to select the right employee for the company, developing a system that improves the accuracy in choosing the right employee based on the Psychology studies and lastly, conducting the usability testing with Human Resource Professionals in order to ensure the success implementation of the system. Besides, the proposed system is mainly designed for the managers and the senior executive in Human Resource Department who involve in the recruitment of the lecturers. SMART Recruiters’ Helper system can also be used for all the computers that have already installed the Microsoft Office Professional Suite of business products. Next, the research methodology used in this project is interview and survey to collect the users’ requirements and the main tool to develop the system will be Microsoft Access 2007. An observation has also been conducted to 7 people in Human Resource department. The finding shows that the system is believed to be able to improve the accuracy of the hiring decision. This whole paper is comprised of the introduction part, the related technologies and studies, the methodology used in completing the project as well as the findings from the survey and interview session

Handwriting Analysis and Personality: A Computerized Study on the Validity of Graphology

Handwriting analysis, also known as graphology, is defined as an analysis of a psychological structure of a human subject through his/her handwriting. It has been applied recently in different fields including areas where making a crucial decision is highly desirable such as forensic evidence, criminology, and disease analysis. However, making a crucial decision based on the results of handwriting analysis is a controversial scientific topic because the validity of graphology rules is still in question. A few validity studies on handwriting analysis have already been conducted earlier using the evaluation of correlation between psychological questionnaires and manual handwriting analysis and they ended up with conflicting results. Manual handwriting analysis suffers from some issues which could be the reasons of the early inconsistent results. Therefore, this study conducted an empirical study that investigates the validation of graphology rules by evaluating the correlation between one of psychological tests named Big Five Factor Markers Test and our proposed automated handwriting analysis system that measures the level of the same big five personality traits based on graphological rules. Our automated BFFM system is called Averaging of SMOTE multi-label SVM-CNN (AvgMlSC). It constructs synthetic samples using Synthetic Minority Oversampling Technique (SMOTE) and averages two learning-based classifiers i.e. Multi-label Support Vector Machine and Multi-label Convolutional Neural Network based on offline handwriting recognition to produce one optimal predictive model. The model is trained using 1066 handwriting samples written in English, French, Chinese, Arabic, and Spanish. The results reveal that our proposed model outperformed the overall performance of five traditional models with 93% predictive accuracy, 0.94 AUC, and 90% F-Score. The statistical test of Spearman’s correlation reports that there is a statistically significant relationship between the score of the big five factors questionnaire and the graphologist’s evaluation for the Big Five Factors with a different strength of relationship. A weak positive relationship is found for Extraversion. However, a moderate positive relationship is reported for Conscientiousness and Open to Experience. On the other hand, a strong positive relationship is indicated for Agreeableness, whilst a very weak positive relationship has been found for the last factor which is Emotional Stability

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK STUDY OF PERSONALITY PREDICTION BASED ON HANDWRITING AND SIGNATURE RECOGNITION USING MULTIPLE ARTIFICIAL NEURAL NETWORK AND MULTI-STR

Abstract: Handwriting Analysis is a scientific method which identify, evaluate and understand an individual's personality. Neurological brain pattern represents the personality trait of a person. Unique neuromuscular movement produced by neurological brain patterns that is the same for every person who has that particular personality trait. Strokes, patterns and pressure applied while writing can reveal specific personality traits. Emotional outlay, fears, honesty and defences are the true personality which are reveled. Handwriting examiners called graphologists analyze handwriting samples for personality detection. There are various techniques to predict the personality. Latest approach uses the graphical approach to predict the personality using Multi-Structure algorithms and Multiple Artificial Neural Networks. This report provides a comparative analysis of various techniques