Facial Emotion Characterization and Detection using Fourier Transform and Machine Learning

TL;DR

This paper introduces a Fourier transform-based machine learning method for facial emotion detection, demonstrating that emotional features are embedded in the frequency domain and can be effectively captured to improve classification accuracy.

Contribution

The paper proposes a novel Fourier transform and frequency kernel approach to extract emotional features from facial images, enhancing ML model performance.

Findings

Achieved over 93% average precision with RF and ANN classifiers.

Validated the hypothesis that emotional features are linearly separable in the frequency domain.

Demonstrated effectiveness using Yale-Faces dataset.

Abstract

We present a Fourier-based machine learning technique that characterizes and detects facial emotions. The main challenging task in the development of machine learning (ML) models for classifying facial emotions is the detection of accurate emotional features from a set of training samples, and the generation of feature vectors for constructing a meaningful feature space and building ML models. In this paper, we hypothesis that the emotional features are hidden in the frequency domain; hence, they can be captured by leveraging the frequency domain and masking techniques. We also make use of the conjecture that a facial emotions are convoluted with the normal facial features and the other emotional features; however, they carry linearly separable spatial frequencies (we call computational emotional frequencies). Hence, we propose a technique by leveraging fast Fourier transform (FFT) and rectangular narrow-band frequency kernels, and the widely used Yale-Faces image dataset. We test the hypothesis using the performance scores of the random forest (RF) and the artificial neural network (ANN) classifiers as the measures to validate the effectiveness of the captured emotional frequencies. Our finding is that the computational emotional frequencies discovered by the proposed approach provides meaningful emotional features that help RF and ANN achieve a high precision scores above 93%, on average.