Facial Emotion Characterization and Detection using Fourier Transform and Machine Learning
This work addresses the challenge of accurate emotion detection from facial images, which is important for applications like human-computer interaction, but it appears incremental as it builds on existing Fourier transform and machine learning methods.
The authors tackled the problem of detecting facial emotions by hypothesizing that emotional features are hidden in the frequency domain, and they developed a Fourier-based technique that achieved average precision scores above 93% using random forest and artificial neural network classifiers.
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.