Real-Time Emotion Recognition using Deep Facial Expression Analysis on Mobile Devices

Main Article Content

Ankur Gupta
Sweta Batra


Numerous applications in human-computer interaction, healthcare, and other fields have been made possible by the growth of mobile devices, which has created new opportunities for real-time emotion recognition. This research introduces a novel method for mobile device-based deep facial expression analysis for real-time emotion recognition. In order to execute accurate and efficient emotion recognition directly on the device, our technology makes use of the computational capacity of contemporary smartphones. This eliminates the need for cloud-based processing and ensures user privacy. For mobile platforms, we use a deep learning architecture that is optimised for both speed and accuracy. A lightweight convolutional neural network (CNN) for facial feature extraction and a recurrent neural network (RNN) for temporal emotion modelling are the main elements of our system. Our system accurately detects and categorises a variety of emotions, including joy, sadness, rage, surprise, and more by processing video frames from the device's camera feed in real-time. We carried out extensive trials on a variety of datasets to assess our methodology, attaining state-of-the-art accuracy with minimal processing cost. Through a variety of applications, including emotion-aware virtual assistants, mental health tracking tools, and immersive gaming experiences, we show how useful our technology is. This paper makes a contribution to the burgeoning field of mobile-based emotion recognition by providing a strong and effective solution that enables researchers and developers to produce ground-breaking software that can better comprehend and react to human emotions while protecting data privacy and guaranteeing real-time performance on mobile devices.

Article Details

How to Cite
Gupta, A. ., & Batra, S. . (2023). Real-Time Emotion Recognition using Deep Facial Expression Analysis on Mobile Devices. Research Journal of Computer Systems and Engineering, 4(2), 89–102.