Modular Deep Learning Framework for Assistive Perception: Gaze, Affect, and Speaker Identification

TL;DR

This paper presents a modular deep learning framework for assistive perception, integrating gaze, affect, and speaker identification through specialized CNN and LSTM models, demonstrating high accuracy on benchmark datasets.

Contribution

It introduces a modular architecture with independent sensing modules for assistive tech, validated by high-accuracy benchmarks, enabling future real-time multimodal integration.

Findings

CNN for eye state detection achieved 93.0% accuracy

Facial expression recognition reached 97.8% accuracy

Speaker identification achieved 96.89% accuracy

Abstract

Developing comprehensive assistive technologies requires the seamless integration of visual and auditory perception. This research evaluates the feasibility of a modular architecture inspired by core functionalities of perceptive systems like 'Smart Eye.' We propose and benchmark three independent sensing modules: a Convolutional Neural Network (CNN) for eye state detection (drowsiness/attention), a deep CNN for facial expression recognition, and a Long Short-Term Memory (LSTM) network for voice-based speaker identification. Utilizing the Eyes Image, FER2013, and customized audio datasets, our models achieved accuracies of 93.0%, 97.8%, and 96.89%, respectively. This study demonstrates that lightweight, domain-specific models can achieve high fidelity on discrete tasks, establishing a validated foundation for future real-time, multimodal integration in resource-constrained assistive devices.