PREDICTING PERSONALITY TRAITS FROM HANDWRITING

ISBN 978-9937-0-9019-3

PREDICTING PERSONALITY TRAITSFROM HANDWRITING

1st Bharat Raj JoshiDepartment of Computer and Electronics Engineering

Kantipur Engineering CollegeLalitpur, Nepal

[email protected]

2nd Sumee AdhikariDepartment of Computer and Electronics Engineering

Kantipur Engineering CollegeLalitpur, Nepal

[email protected]

Abstract—Whenever we listen to or meet a new personwe try to predict personality attributes of the person.Our behavior towards the person is hugely influencedby the predictions we make. Personality is made upof the characteristic patterns of thoughts, feelings andbehaviors that make a person unique. Your personalityaffects your success in the role. Recognizing aboutyourself and reflecting on your personality can help youto understand how you might shape your future. Variousapproaches like personality prediction through speech,facial expression, video, and text are proposed in lit-erature to recognize personality. Personality predictionscan be made out of one’s handwriting as well. The aimof this research project is to examine validity of theGraphological method to assess personality traits. Thisresearch project outlines the development of a SupervisedNeural Network Model for the personality prediction.Machine learning techniques have been widely used invarious fields for complex pattern matching and makingdecisions. The research based system based on neuralnetworks was proposed that aims to determine the BigFive personality traits with handwriting features fromdata sets containing both predefined and random texts.The predefined texts add more value if enforced onwriters in the training stage. Handwriting Analysis orGraphology is a consistent method for perceiving, survey-ing and understanding personality through the strokesand structures revealed by handwriting. Handwriting re-veals authentic character including excited, fears, validityand various others. Capable handwriting experts calledgraphologist every now and again recognize the writerwith the touch of handwriting. This research projectpresents a prediction on a big five personality traits fromhandwriting using FFM and Graphological analysis.

Index Terms—Supervised Neural Network Model,Personality Prediction, Machine Learning, HandwritingAnalysis, Graphology, FFM

I. INTRODUCTION

Handwriting Analysis or Graphology is a scientificmethod of identifying, evaluating and understandingpersonality through the strokes and patterns revealedby handwriting which is supposed to be revealing truepersonality. Handwriting has been used for centuries asa way of communication and expression for humans,but only recently it links to brain activity and the psy-chological aspects of humans have been studied. Thepsychological study of handwriting with the purposeof determining the personality traits[1], psychologicalstates, temperament, or the behavior of the writer is

called graphology and is still a debatable domain as itlacks a standard, most of the handwriting interpreta-tions being done subjectively by trained graphologists.

Handwriting analysis is not a document examina-tion, which involves the examination of a sample ofhandwriting to determine the author. Handwriting isoften referred to as brain writing. Each personalitytrait is represented by a neurological brain pattern[2].Each neurological brain pattern produces a uniqueneuromuscular movement that is the same for everyperson who has that particular personality trait. Whenwriting, these tiny movements occur unconsciously.Each written movement or stroke reveals a specificpersonality trait. Graphology is the science of identi-fying these strokes as they appear in handwriting anddescribe the corresponding personality trait[3]. There-fore, handwriting is, from this perspective, an accuratemirror of people’s brain. The Big Five PersonalityTraits, also known as five-factor model FFM and theOCEAN Model, is a taxonomy for personality traits.

Now, let us be enlightened about personality traitsin detail. What makes someone who they are? Eachperson has an idea of their own personality type – ifthey are bubbly or reserved, sensitive or thick skinned.Psychologists who try to tease out the science of whowe are define personality as individual differencesin the way people tend to think, feel and behave.There are many ways to measure personality, butpsychologists have mostly given upon trying to dividehumanity neatly into types. Instead, they focus onpersonality traits. The Big Five were developed in the1970s by two research teams i.e. Paul Costa and RobertR. McCrae of the National Institutes of Health andWarren Norman and Lewis Goldberg of the Universityof Michigan at Ann Arbor and the University ofOregon[1]. The most widely accepted of these traitswith the handy OCEAN mnemonic are the Big Five:i. Openness ii. Conscientiousness iii. Extraversion iv.Agreeableness v. Neuroticism The Big Five are theingredients that make up each individual’s personality.A person might have a dash of openness, a lot ofconscientiousness, an average amount of extraversion,plenty of agreeableness and almost no neuroticism atall. Or someone could be disagreeable, neurotic, in-

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troverted, conscientious and hardly open at all. Here’swhat each traits entails: i. Openness is shorthand for“openness to experience”. People who are high inopenness enjoy adventure. They’re curious and appre-ciate art, imagination and new things. The motto of theopen individual might be “Variety is the spice of life.”People low in openness are just the opposite: They pre-fer to stick to their habits, avoid new experiences andprobably aren’t the most adventurous eaters. Changingpersonality is usually considered a tough process, butopenness is a personality trait that’s been shown to besubject to change in adulthood. ii. Conscientiousnessis the people who are organized and have a strongsense of duty. They’re dependable, disciplined andachievement-focused. You won’t mind conscientioustypes jetting off on round-the-world journeys with onlya backpack; they’re planners. People low in consci-entiousness are more spontaneous and freewheeling.They may tend toward carelessness. Conscientiousnessis a helpful trait to have, as it has been linked toachievement in school and on the job. iii. Extraversionis possibly the most recognizable personality trait ofthe Big Five. The more of an extrovert someone is,the more of a social butterfly they are. Extrovertsare chatty, sociable and draw energy from crowds.They tend to be assertive and cheerful in their socialinteractions. Whereas, introverts are on the other hand,need plenty of alone time, perhaps because their brainsprocess social interaction differently. Introversion isoften confused with shyness, but the two aren’t thesame. Shyness implies a fear of social interactionsor an inability to function socially. Introverts can beperfectly charming at parties – they just prefer solo orsmall group activities. iv. Agreeableness measures theextent of a person’s warmth and kindness. The moreagreeable someone is, the more likely they are to betrusting, helpful and compassionate. Disagreeable per-son are cold and suspicious of others, and they’re lesslikely to cooperate. Being envious, which can lead topeople being perceived as not agreeable, was found tobe the most common personality type. Envious peoplefeel threatened when someone else is more successfulthan they are. v. Neuroticism is the trait in whichpeople high in neuroticism worry frequently and easilyslip into anxiety and depression. If all is going well,neurotic people tend to find things to worry about.Unsurprisingly, neuroticism is linked with plenty ofbad health outcomes. Neurotic people die youngerthan the emotionally stable, possibly because theyturn to tobacco and alcohol to ease their nerves. Incontrast, people who are low in neuroticism tend tobe emotionally stable and even-keeled. This projectaims to build a system that is able to automaticallyanalyze a set of handwriting features and evaluatethe personality of the writer using the Five-FactorModel (FFM). To test this system the first datasetis proposed, that links the FFM personality traitsto handwriting features. Proposed System offers an

attractive alternative to the standard FFM questionnaireor psychological interviews that are currently usedfor evaluating personality, because it is easier to use,involves less effort and is faster. It aims the highestaccuracy compared to other methods.

II. LITERATURE REVIEW

Handwriting Analysis or Graphology is a scientificmethod of identifying, evaluating and understandingpersonality through the strokes and patterns revealedby handwriting. Among the many aspects of handwrit-ing that can serve as scheme to predict personalitytraits are baseline, size of letter, connecting strokes,spacing between letters, words and lines, startingstrokes, end-strokes, word-slant, speed of handwriting,width of margins, and others. Writer individuality restson the hypothesis that each individual has consistenthandwriting, which is distinct from the handwriting ofanother individual. However, this hypothesis has notbeen subjected to rigorous scrutiny with the accom-panying experimentation, testing, and peer review. Asmentioned previously, currently, there is no standarddeveloped in predicting behavior based on handwrit-ing, the majority of Graphological analysis being doneby specialized graphologists. However, research wasconducted in the area of computer science whichaimed to create such systems in order to recognizethe behavior from Handwriting in an easier way andalso to standardize the Graphological analysis. In thenext paragraphs, we present the state-of-the-art in thisarea as well as several studies which made use ofhandwriting to determine the psychological traits ormental status of individuals.

Mihai Gavrilescu and Nicolae Vizireanu [1] pro-posed the first non-invasive three-layer architecturein literature based on neural networks that aimedto determine the Big Five personality traits of anindividual by analyzing offline handwriting. They alsopresented the first database in literature that links theBig Five personality type with the handwriting featurescollected from 128 subjects containing both predefinedand random texts. Testing their novel architecture onthose database, they showed that the predefined textsadd more value if enforced on writers in the trainingstage, offering accuracies of 84.4in intra-subject testsand 80.5% in inter-subject tests when the randomdataset were used for testing purposes, up to 7% higherthan when random datasets were used in the trainingphase. They obtained the highest prediction accuracyfor Openness to Experience, Extraversion, and Neu-roticism (over 84%), while for Conscientiousness andAgreeableness, the prediction accuracy was around77%.

Behnam Fallah and Hassan Khotanlou describe in[2] a research with a similar purpose as the oneconducted in this paper, aiming to determine the per-sonality of an individual by studying handwriting. TheMinnesota Multiphasic Personality Inventory (MMPI)

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is used for training their system and a Hidden MarkovModel (HMM) is employed for classifying the proper-ties related to the target writer, while a neural network(NN) approach is used for classifying the propertieswhich are not writer-related. The handwriting imageis analyzed by these classifiers and compared with thepatterns from the database, the output being providedin the form of the personality of the writer on theMMPI scale. Their system offers over 70 percentaccuracy at this task.

Similarly, in [3], an instrument for behavioral anal-ysis is described with the task of predicting personal-ity Traits from handwriting. The approach takes intoaccount the following handwriting features: letter “t,”lower loop of the letter “y,” the pen pressure, and theslant of writing.

Similarly, in [4], it is proposed a way to describehandwriting based on geometric features which arecombined using random forest algorithms and kerneldiscriminant analysis. The system is able to predictgender with 75.05 percent, age with 55.76 percent,and nationality with 53.66 percent when all the writerswere asked to write the same text, and 73.59 percentfor gender prediction, 60.62 percent for age prediction,and 47.98 percent for nationality prediction when eachsubject wrote a different text. Since handwriting anal-ysis is a complex task requiring multiple techniques inorder to analyze the multitude of handwriting features.One of the most challenging tasks is the one ofsegmenting the handwritten image into text lines andwords.

Fig. 1: System Mechanism

III. PROBLEM ON HAND

It is difficult to predict personalities of people with-out using an effective system. It takes a whole lotof time and effort to organize FFM questionnaire orpsychological interviews to predict personality. It alsorequires a lot of extra personnel such as psychologiststo do the graphological analysis.

IV. METHODOLOGY

A. System Mechanism

Our system targets the every fields where the per-sonality of person plays an important role. The modeltype that we used is Sequential. Sequential is theeasiest way to build a model in Keras that allows us tobuild a model layer by layer. Our system predicts thepersonality of the people by using their handwritingand classify their personality among the five differenttraits and displays each traits in certain percentagevalue, each of them are out of 100%.

We have performed Two Layer Architecture i.e.Base Layer and Top Layer with Image Augmentationand using CNN respectively. Image Augmentation isused to expand our training dataset artificially bycreating the modified versions of images existing in thedataset. It creates the variations of the image that pro-vides more images to train on. Then they were passedto neural network(CNN) that is composed of multiplebuilding blocks, such as convolution layers, poolinglayers, and fully connected layers, and is designed toautomatically and adaptively learn spatial hierarchiesof features through a backpropagation algorithm. Andthen finally the five personality traits were obtained asoutput from the neural network.

The mechanism of our system is diagrammaticallyshown in the figure below

1) Handwriting Samples: The different types ofhandwritten samples were collected that belong to ourdifferent traits as per their features. The clean andvalidated dataset were downloaded from the kagglewebsite which contained dataset for each traits in

For this, the Vertical Projection Profile (VPP) [5]method has shown the most promising results andthis is the one that we use in this paper for bothrow and word segmentation. Regarding feature clas-sification, different classifiers are used successfullyfor each of the handwriting features. For example,for lowercase letters “t” and “f,” the most commonmethod used is template matching, for writing pressuregray-level thresholding methods are employed, whilefor connecting strokes the Stroke Width Transform(SWT) has shown the best classification accuracycompared to other state-of-the-art methods. In thefollowing sections, we present in detail the classifiersused for each of the handwriting features analyzed inthe current research project. With all these in mind,the current research proposes a novel non-invasiveneural network-based architecture for predicting theBig Five personality traits of a subject by only an-alyzing handwriting. This system would serve as anattractive alternative to the extensive questionnairetypically used to assess the FFM personality traits andwhich is usually cumbersome and non-practical, aswell as avoid the use of invasive sensors. We focusour attention on handwriting because it is an activityfamiliar to almost everyone and can be acquired fastand often.

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Fig. 2: Convolutional Neural Network[6]

separate folders. They were insufficent for the trainingand testing purpose. So in collaboration with oursupervisor we created additional dataset followingthe specified criterias for each traits in the refrencepaper[1]. Then these dataset were used for trainingand the testing purpose. It was made sure that approx-imately 80% of the total dataset were used for thetraining and rest of the 20% dataset were used for thetesting. Accuracy and Loss were the major evaluationcriteria for this model.

2) Image Processing: Image processing is a methodto perform some operations on an image, in orderto get an enhanced image or to extract some usefulinformation from it. Due to the limited number ofdataset we went through image augmentation whichis the part of the image processing. It can be furtherexplained below:

Image Augmentation: It is the technique that weused to expand our training dataset artificially bycreating the modified versions of images existing inthe dataset. It creates the variations of the imagethat provides more images to train on. By creatingthe image generator by calling ImageDataGenerator()function which provides a quick and easy way toaugment images and provides different augmentationtechniques like rotation, flip, shift, shear, zoom, etc.by passing different parameters like shear range forshearing, zoom range for zooming, scale pixels forrescaling were carried out in this process. This taskcomes under the Base Layer.

The dataset was rescaled between 0 and 1 andthen different techniques were applied like horizontalflipping was made true to flip the pictures horizontally.Also the shearing range and zoom range was providedto shear and zoom pictures randomly.

B. Use Neural Network

Convolutional neural network is a class of deeplearning methods which has become dominant invarious computer vision tasks. Convolutional neuralnetwork is composed of multiple building blocks,such as convolution layers, pooling layers, and fullyconnected layers, and is designed to automaticallyand adaptively learn spatial hierarchies of featuresthrough a backpropagation algorithm. The first two,convolution and pooling layers, perform feature ex-traction, whereas the third, a fully connected layer,maps the extracted features into final output, such asclassification. We used Convolutional Neural Network

for the further processing. The tasks under the CNNcan be categorized under two categories viz. FeatureExtraction and the Classification. Going into them indetail:

1) Feature Extraction:1) Convolutional Layer

a) The input images are initially providedto the first layer of feature learning i.econvolutional layer. This layer changes theinput image into the pixels, we can saya 2D picture is changed to 2D form ofimage matrix that contains the differentpixel values. It preserves the relationshipbetween pixels by learning image featuresusing small squares of input data. It alsotakes the filter as an input. By multiplyingthe pixel values contained by image matrixand the filter matrix this layer produces anoutput matrix that contains convolved fea-tures. This multiplication between imagematrix and filter matrix is known as fea-ture mapping. This multiplication betweenimage matrix and filter matrix is known asfeature mapping. Its can be illustrated as:Let height, width and of input image be

Hc,Wc

and of filter matrix be

Hf ,Wf

respectively; Also let the channel size be’C’. Then above expressions can be ex-plained mathematically as:Size of input image :

(Hc ∗Wc ∗ d) (1)

Size of filter :

(Hf ∗Wf ) (2)

Let size of feature map is :

((Hc−Hf +1)∗ (Wc−Wf +1)∗C) (3)

b) The filter moves to the right with a certainStride Value till it parses the completewidth. Moving on, it hops down to thebeginning (left) of the image with the sameStride Value and repeats the process untilthe entire image is traversed. If omitted, thedefault is 1: return every character in therequested range. The default value of strideis used that parses the complete width bymoving filter to right direction with stridevalue 1.

c) The used activation function is ReLU. Itis the widely used activation function thatintroduces the non-linearity. It changes all

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the negative values of output matrix givenby convolution layer to 0. The performanceof ReLU is better in compared to othernonlinear functions like tanh or sigmoid.Mathematically, it can be shown as:

R(z) = max(0, X) (4)

where X is the initial value before passingto activation function as input. The max-imum value between O and X is outputvalue from this activation function.

ii. Max pooling was used to reduce the spatialdimensions of the output volume. This layer isresponsible for reducing the parameters of theimages specially when they are very large. It canbe done in many ways like max pooling, averagepooling and sum pooling. Max Pooling returnsthe maximum value from the portion of theimage covered by the Kernel. On the other hand,Average Pooling returns the average of all thevalues from the portion of the image covered bythe Kernel. Max pooling was used in our model.It uses the maximum value out of differentvalues under the pool size provided. Generallypool size of 2*2 is used that mostly results in 4values replaced by one maximum value amongthem. In this case, the dimensionality of the 2darray from convolutional layer is reduced to half.If we have feature map of size

Hf ∗Wf

then after pooling with pool size P they arereduced as

(Hf

P∗ Wf

P) (5)

, whereHf ,Wf

have even value if they have odd value then first1 is subtracted from them and then they are feedto above equation (4.4).This ‘Feature Learning’ process can be repeatedas many times as per requirement until satisfied.Number of repetations can be analyzed by look-ing at the size of the picture to be used andaccording to it they can be segregated. Aftercompleting all the repetitions now the result ispassed to the ‘classification’ category of the con-volutional Neural Network thich comes underTop Layer which is described under algorithm.

V. BACKPROPAGATION ALGORITHM

As the task is a pattern recognition task and alsoconsidering that our architecture is bottom-up with nofeedback loops, we use a feed-forward neural network.Also, with the same premises in mind, the trainingmethod will be used is backpropagation, which hasproven to be very effective and offers fast learning. Theback- propagation algorithm cycles through a forward

Fig. 3: Neural Network Structure[1]

pass followed by a backward pass through the network.The algorithm alternates between these passes until it“learns” to make good classifications. Forward Pass:In this pass outputs of all neurons in the network arecomputed. Neuron outputs are computed for each layerfrom the first hidden layer to the subsequent layers.Outputs of one layer are the inputs of the succeedinglayer. This continues layer by layer until we reach theoutput layer and compute the outputs for this layer.Backward Pass: The error calculated in the forwardpass is propagated backwards from the output layerto the input layer. Weight modification is done as theerror is propagated.

1) Back Propagation: Detail Algorithm:i. Initialize the weights and bias to random values.

ii. Consider input values I and target value T .iii. Do forward pass to calculate hidden layer values

Ij =∑

(wij ∗Oi) + bj (6)

iv. Take a activation function for input to hiddenlayer

R(z) = max(0, x) (7)

v. Calculate input for output layer

Ik =∑

(wjk ∗Oj) + bk (8)

vi. Take a activation function for hidden to outputlayer i.e Sigmoid activation function.

f(x) =1

1 + e−Ik(9)

vii. Calculate Errors:• Error in output layer

Ek = Ok(1 −Ok)(T −Ok) (10)

• Error in hidden layer

Ej = Oj(1 −Oj)∑

(Ek ∗ wjk) (11)

viii. Updating Weight and bias• Hidden layer’s weight and bias are updated

using following formula:

∆(wij) = α ∗ Ej ∗Oi (12)

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wij(new) = wij(old) + ∆(wjk) (13)

∆(bj) = α ∗ Ej (14)

bj(new) = bj + ∆(bj) (15)

• Output layer’s weight and bias are updatedusing following formula:

∆(wjk) = α ∗ Ek ∗Oj (16)

wjk(new) = wjk(old) + ∆(wjk) (17)

∆(bk) = α ∗ Ek (18)

bk(new) = bk(old) + ∆(bk) (19)

ix. Continue from step (ii) to (vi) untill all trainingsets values are satisfied.

As the algorithms name implies, the errors (and there-fore the learning) propagate backwards from the outputnodes to the inner nodes. Therefore, backpropaga-tion is used to calculate the gradient of the error ofthe network with respect to the networks modifiableweights. This gradient is almost always then used insimple stochastic gradient descent algorithm to findweights that minimize the error. Often the term ”back-propagation” is used in a more general sense, torefer to the entire procedure encompassing both thecalculation of the gradient and its using stochastic gra-dient descent. Back-propagation usually allows quickconvergence on satisfactory local minima for error inthe kind of networks to which it is suited.

2) Implementation of Algorithm: In our project wecreated a model using a backpropagation algorithmforming one input layer and one hidden layer withactivation function ’relu’, which is a default activationfunction for many types of neural network as it iseasier to train and achieve better performance. Thisactivation function uses piecewise linear function thatwill output the input directly if it is positive, otherwise,it will output zero. Along with input and hidden layerswe have output layer that is activated by sigmoidactivation function which is mostly preferred in outputlayers since it gives multiple true answers in our modeli.e classifying into five traits. We used Flatten as aconnection between the convolution and dense layers.

We compiled our model by taking three parametersi.e ’adam’ as a optimizer that adjusts the learning ratethroughout training, ’sparse categorical crossentropy’as our loss function that saves time in memory as wellas computation because it simply uses a single integerfor a class, rather than a whole vector and ’accuracy’ asour metric to see the accuracy score on the validationset when we train the model.

VI. RESULT

Our main objective was to build a system that canpredict the personality by using the handwriting ofpeople under 5 major traits. We collected dataset foreach personality traits which was the most difficulttask while carrying out project. Since we had notenough dataset we performed image augmentation thathelped us to expand our training dataset by creatingthe modified versions of images that we had collected.The different variations were done in this category likeshearing, zooming which were done randomly, flippingthat was done horizontally. To ensure the size of databetween 0 and 1 rescaling process was carried out.Then after this task of image processing we build asequential model in which we can created our modellayer-by-layer. Under the convolutional neural networkthe data or image went through those layers repeatedly.The process can be described with an example asshown below:

VII. FEATURE EXTRACTION PHASE

i. Convolutional LayerIn this layer the image is changed to pixel valuein 2D array. For eg, though the dimension of theimages is high for easy understanding, let us saythe image is converted to 6*6 2d array of pixelsas shown below:

0 1 0 0 1 01 0 1 0 1 00 0 1 0 1 00 0 1 0 1 10 0 0 1 1 00 1 0 1 0 1

TABLE I: Image sample changed to 2D matrix of pixelvalue

Also we had used a filters of size 3*3 which ismultiplied by the convolutional matrix to obtainthe feature map. For example, let is consider afilter matrix be as shown below :

0 1 01 0 10 0 1

TABLE II: Filter matrix of size 3*3

Then to extract the feature map the above 2dconvolutional matrix was multiplied by the 3*3filter size matrix . We obtained a matrix as shownbelow:

4 0 3 12 1 3 11 2 3 20 3 1 3

TABLE III: Extracted Feature Map

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eg, if the provided image size is (600*600*3)and the filter size is (3*3) then the feature mapwill be,Feature Map size = ((600-3+1)*(600-3+1)*8)Feature Map size = 598*598*8

ii. Activation functionThe activation function called ReLU was usedwhich was resposible for changing all negativevalues to the zero. It can be described by takingan valid example as shown below. All the pixelvalue of feature set which were negative valueswere changed to 0 value as shown below. Thearray or matrix obtained in this way were thensent to next layer, pooling layer for furtherprocessing.

-4 0 3 -1 1 06 -4 0 1 -1 32 4 -1 0 -2 -43 0 -1 2 -1 41 3 -2 1 0 2-1 0 -2 2 1 -3

TABLE IV: Array before applying ReLU function

0 0 3 0 1 06 0 0 1 0 32 4 0 0 0 03 0 0 2 0 41 3 0 1 0 20 0 0 2 1 0

TABLE V: Array After applying ReLU function

iii. PoolingNow this matrix undergoes a pooling layer. Thepooling can be done as max pooling, averagepooling or sum polling but here we preferredmax pooling of size 2*2. The dimension ofabove matrix decreases after pooling withoutdistrubing its features. The matrix produced istabulated below

6 3 34 2 43 2 2

TABLE VI: Max pooling using pool size 2*2

If the provided size of feature map to poolinglayer is (598*598*8) and the pool size is (2*2)then the 2d array after pooling will be of size,Pooled size = ((598/2)*(598/2)*8)Pooled size = (299*299*8)If the size of feature map to pooling layer is(297*297*16) and the pool size is (2*2) thenthe 2d array after pooling will be of sizePooled size = (((297-1)/2)*((297-1)/2)*16),since feature map has dimension of odd value,first 1 is subtracted from feature map and thencalculation is done.

Pooled size = (148*148*16) ,

The above process i.e sequence of going throughconvolutional layer, normalization layer andpooling layer was repeated for five more times.Since our input images has the size 600*600*3we went to repeat this cycle for 5 more times.And finally we got comparatively small sized 2Darray ie matrix that was changed to 1D arrayby flattening using flatten() function. Then sofound data were feed into the dense layer ofthe classification category of the convolutionalneural network which is an input layer for clas-sification algorithm. for example the flatten arrayof the above matrix looks like:

633424322

TABLE VII: Array from pooling layer after flattening

These values are taken as input for the inputlayers of the classification algorithm and furthercalcuation or process takes places which is de-scribed below in the classification section.

VIII. CLASSIFICATION PHASE

The classification category was done using the back-propagation algorithm. Each steps with example isdescribed as:

For simplicity, Let’s assume that we have input layerwith five input nodes, hidden layer with two hiddennodes and one output layer with one node .

i. Input Layer: Input layer gets all the values flat-tened, from ‘feature extracting’ category. In thislayer no mathematical calculations were done.The values were passed to hidden layers. Forsimplicity let’s consider we have five input nodeswith values 6, 3, 4, 2, 4

ii. Hidden Layer: Hidden layers get the certainvalues which were calculated using input valuesand the random weight along with the randombias value between 0 and 1.

• The value of two nodes of hidden layer werecalculated as:

H1 = w11 ∗ I1 +w21 ∗ I2 +w31 ∗ I3 +w41 ∗ I4+w51 ∗ I5 + bias1

H1 = 0.2∗6+0.3∗3+0.3∗4+0.15∗2+0.25∗4+0.15

Th random values of weights and bias were usedas above and we obtained value as H1 = 4.75Similar calculation was done for H2 and weobtained H2 = 4.57

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• Now the output from the hidden layers werecalculated by using the relu activation functionwe obtained asOH1 = 4.75, OH2 = 4.57The negative values if found any were changedto 0 by relu function.

iii. The input to the output layer were also calculatedas

O = OH1 ∗ w21 +OH2 ∗ w22 + bias2

O = 4.75 ∗ 0.1 + 4.56 ∗ 0.15 + 0.2

O = 1.359

iv. The output from the output node was calculatedusing its sigmoid activation function as:

Oo =1

1 + e−O

Oo = 0.7956

v. Error calculation

• Then error at output node was calculated as

EO = Oo(1 −Oo)(T −Oo)

EO = 0.7956(1 − 0.7956)(0 − 0.7956)

EO = −0.1293

• The error at hidden layer was also calculated as

EH1 = O(1 −O)∑

(Eo ∗ w21)

EH1 = 4.75(1 − 4.75) ∗ (−0.1293) ∗ 0.1

EH1 = 0.2303

also

EH2 = 4.57(1 − 4.57) ∗ (−0.1293) ∗ 0.15

EH2 = 0.3164

vi. After calculating the error the weight and biasupdating process was carried out as

∆w11 = 0.1 ∗ (0.2303) ∗ 6

∆w11 = 0.1398

Hence,w11new = 0.1398 + 0.2

w11new = 0.33818

Also the bias was updated as

∆(bias1) = 0.1 ∗ (0.1398)

∆(bias1) = 0.01398

bias1new = 0.15 + (0.01398)

bias1new = 0.16398

Similarly other weights w12, w13, w14, w14 andbias were updated.Also the weight and bias of the output layer’snode were updated as

∆w21 = 0.1 ∗ (−0.1293) ∗ 4.75

∆w21 = −0.06142

Hence,

w21new = −0.06142 + 0.2

w21new = 0.1385

Also the bias2 was also updated as

∆(bias2) = 0.1 ∗ (−0.06142)

∆(bias2) = −0.006142

bias2new = 0.15 + (−0.006142)

bias2new = 0.143858

Similarly w22 was also updated along with itsbias.This process was repeated for other trainingdataset and later we continue this for more epoch(15 in our model) to obtain much accurate solu-tion and finally the output values were obtainedfrom five output nodes.And when the picture was provided to the modelto test the personality traits, picture goes underall above processes of Convolutional Neural Net-work and at last the output value of trained dataand this data to be tested were compared andwith respect to value the output was produced.The sigmoid activation function was used in thiscase since our model gives the multiple truevalues as output. Sigmoid was best choice sincesystem displays the each amount of traits thathandwriting consists of each per cent, neitherthe total sum of all traits becomes 100 nor itdisplays just one trait as maximum and other asminimum in our system.

IX. MODEL SUMMARY

The CNN model that was developed had six lay-ers repeating in sequence for feature extraction thatare convolutional layer and max pooling layer. Aftercompleting six cycles then the value obtained as twodimesional array were flattened to get one dimensionalarray. Then the each values were passed to the inputlayer of the classification phase where no furthermathematical calculations were carried out and thenfurther calculations for other layers were carried out asdiscussed above. The CNN model can be summarizedas shown in given figure that shows the output shapealong with its respective layer:

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Fig. 4: Summary of CNN Model

Fig. 5: Training accuracy and validation accuracy plot

X. ACCURACY AND LOSS

The figure below shows the accuracy plot of oursystem that tabulates the progress on accuracy withrespect to number of epoch. The training accuracy ofsystem was approximately 85%.The testing accuracyincreased from 64% initially to the 84% at the lastepoch.

Also the testing and validation loss are shown below.The validation loss was high due to the more numberof outliers in our dataset. We got testing loss less thanvalidation loss which was positive point for us andit is decreasing as the number of epochs increases asshown in graph.

Fig. 6: Training loss and validation loss plot

XI. LIMITATIONS

Despite of some changes in the initial plans theobjectives were fulfilled and output was observed. Thesystem have few limitations as well, that are:

i. Our system doesn’t give accurate result for thelanguages other than English.

ii. It doesn’t displays the accurate result for typedand printed text pictures.

XII. FUTURE ENHANCEMENT

Despite of some limitations, we were able to achievetargeted objectives and develop our system which canbe enhanced in future.

i. The system can be used for different languagesby using dataset of different languages.

ii. The model can give specific trait with maximumvalue as output.

XIII. CONCLUSION

The training accuracy of system was approximately85%.The testing accuracy increased from 64% initiallyto the 84% at the last epoch. The validation loss washigh due to the more number of outliers in our dataset.We got testing loss less than validation loss whichwas encouraging and it is decreasing as the numberof epochs increases. Our system doesn’t give accurateresult for the languages other than English but thissystem can be extended to support different languagesby using dataset of different required languages.

ACKNOWLEDGMENT

First and foremost, we would like to thank Com-puter and Electronics Department of Kantipur Engi-neering College for giving us this opportunity to pre-pare this project. We would like to express our sinceregratitude towards Er. Rabindra Khati, HOD for hisprecious encouragement. We would like to thank Er.Anup Kc, Deputy HOD, for his constant guidance andinspiring lectures. We are highly indepted to Er. DipeshShrestha and Er. Bishal Thapa for co-operating with us

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and letting us carry out this project smoothly.Also. wewould like to thank our supervisor Er. Ujjwal Prajapatifor guiding us in completion of this project. Withouthis supervision and suggestions, it would have beendifficult for us.

Last but not the least, we are thankful to all ourteachers and people who have contributed in prepara-tion of this project. We have tried to make this projectas accurate as possible. Suggestions and commentsfrom readers are highly appreciable.

REFERENCES

[1] Gavrilescu, Mihai & Vizireanu, Nicolae. (2018). Predicting theBig Five personality traits from handwriting. EURASIP Jour-nal on Image and Video Processing. 2018. 10.1186/s13640-018-0297-3.

[2] B Fallah, H Khotanlou, in Artificial Intelligence and Robotics(IRANOPEN). Identify human personality parameters basedon handwriting using neural networks (April 2016)

[3] HN Champa, KR Anandakumar, Automated human behaviorprediction through handwriting analysis. 2010 First Interna-tional Conference on Integrated Intelligent Computing (ICIIC),160–165 (August 2010)

[4] S Maadeed, A Hassaine, Automatic prediction of age, gender,and nationality in offline handwriting. EURASIP Journal onImage and Video Processing 2014, 10 (December 2014)

[5] V Papavassiliou, T Stafylakis, V Katsouro, G Carayannis,Handwritten document image segmentation into text lines andwords. Pattern Recogn. 43, 369–377 (2010)

[6] A Yu, How to teach a computer to see with ConvolutionalNetworks.( November 2018)

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Documents

PREDICTING PERSONALITY TRAITS FROM HANDWRITING