Multiple Intelligence Evaluation Using Dermatoglyphics for Identifying Leardarad Types and Personality in Children

Nowadays, people are more concerned about understanding their children's leardarad types and personality traits. Every individual has a unique personality and innate potential. It is very important to identify inborn talent which can further help to understand the course of interest at a young age. So that they can make the most of the available opportunities in life. Understanding someone's personality can help us to identify in which field they can perform well. The goal of this research paper is to automate the time-consuming manual work. Our research suggests a better way to identify leardarad type and personality based on the fingerprint. Certain elements of brain development and fingerprints are simultaneously produced during fetal development by the combination of hereditary and environmental variables.With the help of dermatoglyphics, we can recognize innate potentials, multiple intelligences, and personality traits from the fingerprints of the person. The suggested system takes scanned fingerprints as input. These scanned images undergo image processing. The images are converted into black and white format using grayscaling. These images are then filtered using a bilateral filter. Morphological operations are also performed on these images. The Gabor filtering technique is used for image enhancement. Features are extracted and dimensionality is reduced using Principle component analysis. A convolutional neural network is trained to accurately identify into seven categories. GradCam technique is used for model evaluation. Thus, we generate a report based on the identified finger type to recommend the leardarad type and personality trait.