Thesis (Ph.D) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department

Includes bibliography (sheets 95-111)

ABSTRACT
It has long been known that handwriting reveals many information for an individual's
identification. For the last century, investigators first tried to predict the owner of the
handwriting then recognized that it reveals more information such as the characteristics
of the owner therefore shifted most of their attention to personal characteristics
estimation, such as emotional state, However, to be able to evaluate and investigate the
emotional state of an individual there is a need of a database with correctly labelled data
with the relevant ground truth information. Yet, for researchers to be able to evaluate
one's emotional state fairly is to work on a database providing the basic requirements
which are, including contributors' demographic labels (age, gender, ethnicity etc.) or
emotional status in addition to their identification labels. Unfortunately, there are almost
no database, neither public nor commercial, providing this useful information.
This dissertation will first propose a novel and rigorous handwriting and signature
biometric database both in offline and online form and then predict one’s emotional state.
To achieve this goal wide range of ground truths (i.e. identity labels, demographic labels
as well as emotional state labels) will be used which are not contemplated by any other
publicly or commercially available biometric handwriting and signature databases in the
literature. Therefore, making this novel database available will shed a light to the
biometric research community once again and researchers can actively work on it to
contribute to science.
The proposed database comprised total of 134 volunteers with handwriting and signature
biometric data acquired using a digitizing tablet and a stylus pen. The process consisted
of four different steps, each with aiming to extract different emotional states Task 1 -
natural, Task 2 - happy, Task 3 - sad and Task 4 - stress. The database presented also
includes individuals’ demographic information such as age, gender, handedness,
education level and nationality. Database in total consists of 804 handwriting and 8040
signature biometric samples.
To be able to achieve the goal of this dissertation there have been 3 different experiments
carried out to predict emotional state from handwriting and signature biometrics and the
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results obtained were encouraging with slightly high recognition rates and proves that
both handwriting and signature biometrics contain valuable information regarding one’s
emotional state. Out of 42 features 11 of them were selected after the feature selection
method for classification purpose.