Human-Computer Interaction through Hybrid Inception V3 and LSTM based Facial Emotion Recognition: An Experimental Study
Year: 2026
Type: article
Abstract: Facial emotion recognition (FER) plays a crucial role in areas like healthcare, security, and human–computer interaction. In this work, we introduce a deep learning model that uses Inception-V3 for feature extraction and a Bi-directional LSTM for sequence learning. The idea is to capture both spatial patterns from faces and the temporal relationships between expressions. The model was tested on the FER-2013 dataset using subject-independent evaluation to check generalization. For comparison, results from common CNN models such as ResNet-50, MobileNet-V2, and a plain Inception-V3 were utilized. The proposed hybrid system reached about 94.5% accuracy, showing that adding BiLSTM improves recognition compared to CNNs alone. Our results point out that combining convolutional networks with sequence models can boost performance in emotion recognition tasks.
Source: Premier journal of science.
DOI: 10.70389/pjs.100213
Authors: Shylaja Purakkadavath, Thomas Chacko
Institution: Kannur University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7126022124