A Portable Personality Recognizer Based on Affective State Classification Using Spectral Fusion of Features

Abstract

In this paper, we introduce a system named Portable Personality Recognizer (PPR), which classifies the personality of an individual using his/her transitions of affective states. This work attempts to reveal the latent relationship between emotions and personality of a person. Here, we train a hidden Markov model (HMM) with observable emotional states viz. Happiness (H), Anger (A), Surprise (S) and Disgust (D) and the hidden traits viz. Psychoticism (P), Extraversion (E) and Neuroticism (N). Based on the model, the system estimates the personality as Psychotic, Extravert or Neurotic. It does so by capturing the facial images of an individual using a visible and a thermal camera to decide the present affective state of the person. The emotion classification is carried out using fused eigenfeatures from the visible and blood perfused thermal images. The emotional state changes are observed using the trained HMM to estimate the personality. The proposed hardware prototype consists of a Banana Pi board with a seven inch LCD screen having a thermal and a visible camera add-ons. The system achieves an emotion classification accuracy of 87.145 percent, while an accuracy of 87.87 percent is achieved for personality recognition.