Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition

Year : 2025 | Volume : 03 | Issue : 02 | Page : 24 30
    By

    Sakshi Bhaganagare,

  • Shravani Chavan,

  • Sonali Gavali,

  • Vaibhav Godase,

  1. Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  4. Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India

Abstract

Recent progress in solid-state electronics and embedded vision systems has enabled real-time emotion-aware applications at the edge. This paper presents MoodMate, a solid-state edge-AI framework for real-time facial emotion recognition using camera-based sensing and embedded processing. The proposed system integrates a solid-state image sensor with an AI- driven emotion classification pipeline optimized for low-latency and resource-constrained environments. Intelligent, emotion-aware apps can now be deployed right at the network edge thanks to recent developments in solid-state electronics, low-power embedded CPUs, and camera-based vision systems. This study introduces MoodMate, a solid-state edge artificial intelligence system that uses embedded vision and on-device computation to recognize face emotions in real time. The suggested system combines an efficient AI-driven emotion classification pipeline designed for low-latency operation in resource-constrained embedded contexts with a small solid-state image sensor. Convolutional neural network-based models that are fully implemented on embedded hardware platforms are used to perform real-time face detection, facial feature extraction, and emotion classification on facial pictures obtained through an embedded camera module. The solution greatly lowers inference latency while improving data privacy and operational reliability by doing away with reliance on cloud- based processing. Facial images captured through a camera module are processed using convolutional neural network–based feature extraction and emotion classification models deployed on embedded hardware platforms. The system performs real-time face detection, facial feature analysis, and emotion inference without reliance on cloud computation, thereby ensuring reduced latency and improved data privacy. Experimental results demonstrate accurate recognition of primary human emotions such as happiness, sadness, fear, and neutrality under real-time conditions. The proposed solid-state implementation highlights the feasibility of deploying emotion-aware intelligence on embedded platforms for applications in smart healthcare, assistive systems, and human–computer interaction.

Keywords: Solid-state embedded systems; edge AI; real-time emotion recognition; facial expression analysis; camera-based sensing; embedded vision; affective computing

[This article belongs to International Journal of Solid State Innovations & Research ]

How to cite this article:
Sakshi Bhaganagare, Shravani Chavan, Sonali Gavali, Vaibhav Godase. Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition. International Journal of Solid State Innovations & Research. 2025; 03(02):24-30.
How to cite this URL:
Sakshi Bhaganagare, Shravani Chavan, Sonali Gavali, Vaibhav Godase. Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition. International Journal of Solid State Innovations & Research. 2025; 03(02):24-30. Available from: https://journals.stmjournals.com/ijssir/article=2025/view=235424


References

  1. Liao D, Ai W. VI-NeRF-SLAM: a real-time visual–inertial SLAM with NeRF mapping. Journal of Real-Time Image Processing. 2024 Apr;21(2):30.
  2. Qi F, Wang Y, Tang Z, Chen S. Real-time and effective detection of agricultural pest using an improved YOLOv5 network. Journal of Real-Time Image Processing. 2023 Apr;20(2):33.
  3. Rezaei B, Mobasseri M, Enayatifar R. A secure, efficient and super-fast chaos- based image encryption algorithm for real-time applications. Journal of Real-Time Image Processing. 2023 Apr;20(2):30.
  4. Ekambaram D, Ponnusamy V. Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder. Journal of Real-Time Image Processing. 2024 Feb;21(1):2.
  5. Elhanashi A, Saponara S, Dini P, Zheng Q, Morita D, Raytchev B. An integrated and real-time social distancing, mask detection, and facial temperature video measurement system for pandemic monitoring. Journal of Real-Time Image Processing. 2023 Oct;20(5):95.
  6. Saponara S, Elhanashi A, Zheng Q. Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19. Journal of Real-Time Image Processing. 2022 Jun;19(3):551-63.
  7. Saurav S, Gidde P, Saini R, Singh S. Real-time eye state recognition using dual convolutional neural network ensemble. Journal of Real-Time Image Processing. 2022 Jun;19(3):607-22.
  8. Zhu Y, Dai Y, Han K, Wang J, Hu J. An efficient bicubic interpolation implementation for real-time image processing using hybrid computing. Journal of Real-Time Image Processing. 2022 Dec;19(6):1211-23.
  9. Saponara S, Elhanashi A, Gagliardi A. Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19. Journal of Real- Time Image Processing. 2021 Dec;18(6):1937-47.
  10. Kaibou R, Azzaz MS, Benssalah M, Teguig D, Hamil H, Merah A, Akrour MT. Real-time FPGA implementation of a secure chaos-based digital crypto- watermarking system in the DWT domain using co-design approach. Journal of Real-Time Image Processing. 2021 Dec;18(6):2009-25.
  11. Wang S, Zhao J, Ta N, Zhao X, Xiao M, Wei H. A real-time deep learning forest fire monitoring algorithm based on an improved Pruned+ KD model. Journal of Real-Time Image Processing. 2021 Dec;18(6):2319-29.
  12. Lai Z, Chen L, Jeon G, Liu Z, Zhong R, Yang X. Real-time and effective pan- sharpening for remote sensing using multi-scale fusion network. Journal of Real- Time Image Processing. 2021 Oct;18(5):1635-51.
  13. Tariverdi A, Venkiteswaran VK, Richter M, Elle OJ, Tørresen J, Mathiassen K, Misra S, Martinsen ØG. A recurrent neural-network-based real-time dynamic model for soft continuum manipulators. Frontiers in Robotics and AI. 2021 Mar 18;8:631303.
  14. Saponara S, Elhanashi A, Gagliardi A. Real-time video fire/smoke detection based on CNN in antifire surveillance systems. Journal of Real-Time Image Processing. 2021 Jun;18(3):889-900.
  15. Raufmehr F, Salehi MR, Abiri E. A frame-level MLP-based bit-rate controller for real-time video transmission using VVC standard. Journal of Real-Time Image Processing. 2021 Jun;18(3):751-63.
  16. Shah AA, Parah SA, Rashid M, Elhoseny M. Efficient image encryption scheme based on generalized logistic map for real time image processing. Journal of Real- Time Image Processing. 2020 Dec;17(6):2139-51.
  17. Pérez-Patricio M, Aguilar-González A. FPGA implementation of an efficient similarity-based adaptive window algorithm for real-time stereo matching. Journal of Real-Time Image Processing. 2019 Apr 1;16(2):271-87.
  18. Sinhal R, Ansari IA, Jain DK. Real-time watermark reconstruction for the identification of source information based on deep neural network. Journal of Real- Time Image Processing. 2020 Dec;17(6):2077-95.
  19. Cambuim LF, Oliveira Jr LA, Barros EN, Ferreira AP. An FPGA-based real-time occlusion robust stereo vision system using semi-global matching. Journal of Real- Time Image Processing. 2020 Oct;17(5):1447-68.
  20. Njiki M, Elouardi A, Bouaziz S, Casula O, Roy O. A multi-FPGA architecture- based real-time TFM ultrasound imaging. Journal of Real-Time Image Processing. 2019 Apr 1;16(2):505-21.
  21. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
  22. Godase V, Godase J. Diet prediction and feature importance of gut microbiome using machine learning. Evolution in Electrical and Electronic Engineering. 2024 Nov 6;5(2):214-9.
  23. Jamadade VK, Ghodke MG, Katakdhond SS, Godase V. A Comprehensive Review on Scalable Arduino Radar Platform for Real-time Object Detection and Mapping.
  24. Godase V. A comprehensive study of revolutionizing EV charging with solar- powered wireless solutions. Advance Research in Power Electronics and Devices e- ISSN. 2025 Apr 18:3048-7145.
  25. Godase V. Advanced Neural Network Models for Optimal Energy Management in Microgrids with Integrated Electric Vehicles. InProceedings of the International Conference on Trends in Material Science and Inventive Materials (ICTMIM- 2025) DVD Part Number: CFP250J1-DVD 2025 Apr 18.
Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 15/12/2025
Accepted 16/12/2025
Published 31/12/2025
Publication Time 16 Days
141

Login


My IP

PlumX Metrics