An Adaptive Bacterial Foraging Algorithm Based Faster Region–CNN for Classifying Personality Traits

Abstract

Mechanized personality discovery from image features has arisen and acquired a lot of consideration in the branch of knowledge of full of feeling registering and opinion examination. This work presents a deep learning model that can measure personality characteristics on five classes which is Conscientiousness (CON), Openness to experience (OPN), Extraversion (EXT),  Neuroticism (NEU) and Agreeableness (AGR) from a picture. This work proposes a model utilizing Convolutional Neural Networks to naturally extract highlights from a representation that are marks of personality qualities. To improve the detection level of personality Traits, Faster Region Convolutional Neural Network (F-CNN) model with Adaptive Bacterial Foraging Optimization (ABFO) is presented. The proposed model exhibits the effectiveness of the acquainted technique with a promising personality forecast model and can group the client's personality qualities when contrasted with the best-in-class procedures. While assessing the proposed technique, results show a ruthless and critical exactness improvement in contrast with the latest outcomes for the Personality dataset for personality recognition. Moreover, present the usually utilized datasets and call attention to a portion of the difficulties of personality-mindful proposal frameworks.