Source of Publication
Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the Softmax layer in the EfficientNet with the KNN. We train our model using the FER-2013 dataset and compare its performance with different architectures trained on the same dataset. We perform optimization on the Fully Connected layer, loss function, loss optimizer, optimizer learning rate, class weights, and KNN distance function with the k-value. Despite running on the Raspberry Pi hardware with very limited processing power, low memory capacity, and small storage capacity, our proposed model achieves a similar accuracy of 75.26% (with a slight improvement of 0.06%) to the state-of-the-art’s Ensemble of 8 CNN model.
Institute of Electrical and Electronics Engineers (IEEE)
Computational modeling, Convolutional neural networks, EfficientNet-Lite, emotion recognition, facial expression recognition, Feature extraction, Hidden Markov models, hybrid CNN-KNN, Raspberry Pi, Real-time systems, Support vector machines, Training
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Wahab, Mohd Nadhir Ab; Ren, Anthony Tan Zhen; Nazir, Amril; Noor, Mohd Halim Mohd; Akbar, Muhammad Firdaus; and Mohamed, Ahmad Sufril Azlan, "EfficientNet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi" (2021). All Works. 4515.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series