Personality Identification Through Facial Features by Using Neural Networks for Pakistani People

Abstract

Computational physiognomy, also known as digital face reading, is a concept that uses automatic computational methods to recognize a person's personality traits, psychological qualities, or mentality based on their outward appearance, including structural, texture, or color-based face features. It has been one of the most exciting research topics in the last decade, not only for computer scientists but also for psychologists. Previously, an expert physiognomist measured all face attributes manually. However, as computational technologies, image processing techniques, and machine learning algorithms have advanced over the last decade, the physiognomy approach has shifted toward automatic personality analysis systems that can generate an individual's entire personality report using a single face image as input. Computational physiognomy solutions have already been proposed in China, Taiwan, Australia, Singapore, Korea, and Poland for a variety of applications; however, they only incorporate datasets for their people and are not publicly available. Furthermore, the measurement-based approach extracted a very limited number of features, whereas the neural network approach used a non-uniform distribution of feature classes, which does not match generic or modern physiognomy literature. In this thesis, we intend to investigate modern physiognomy principles, create a local dataset, and develop a prototype of an automatic personality identification system. We studied modern physiognomic rules and labeled a dataset of about 240 images for 10 different features. In addition, we investigated the measurement-based approach and proposed and developed an improved methodology for extracting face features from nearly any type of image. We also increased the number of features from 3 to 7, modified the calculation method with a cutting-edge machine learning-based landmarks detection model, and achieved a classification accuracy of 70% to 80% for each feature. Finally, we generated the ultimate personality report by comparing classification results to the personality trait knowledge library.