IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

SMART FACE RECOGNITION WITH EMOTION AND RELEVANCE FEEDBACK

B Navya G Abhiram K Harshitha T Sai Pradeep

A Suruthi Sutharsana

Department of CSE (Data Science)  ACE Engineering College Hyderabad Telangana India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

Face recognition systems are widely used in applications such as surveillance and smart environments, but most existing approaches focus only on identifying individuals and do not consider emotional context or group-level analysis. This project presents a smart face recognition system that can detect and recognize multiple persons simultaneously while also identifying their facial emotions from images or live video streams using deep learning techniques. The system aggregates individual recognition and emotion results to generate overall feedback representing the collective emotional state and recognition accuracy of the group. A relevance feedback mechanism allows the system to learn from user corrections and continuously improve performance over time. This adaptive and emotion-aware approach enhances reliability and makes the system suitable for applications such as smart classrooms, attendance monitoring, and intelligent surveillance.

How to Cite this Paper

Navya, B., Abhiram, G., Harshitha, K. & Pradeep, T. S. (2026). Smart Face Recognition with Emotion and Relevance Feedback. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.182

Navya, B, et al.. "Smart Face Recognition with Emotion and Relevance Feedback." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.182.

Navya, B,G Abhiram,K Harshitha, and T Pradeep. "Smart Face Recognition with Emotion and Relevance Feedback." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.182.

Search & Index

References


  1. Gupta, A., Wang, M., & Hernandez, S. (2025). Deep Learning for Real-Time Multi-Person Emotion and Identity Recognition.
    Journal: IEEE Transactions on Affective Computing (early access / proposed study).

  2. Li, C., Singh, R., & Khan, A. (2024). Adaptive Face Recognition with Feedback Learning.
    Journal: Applied Artificial Intelligence.

  3. Patel, R., & Sharma, K. (2023). Real-Time Emotion Detection Using Edge AI.
    Journal: International Journal of Computer Vision Applications.

  4. Li, H., & Sun, J. (2022). 3D Facial Expression Recognition Using Deep Learning.
    Journal: Pattern Recognition Letters.

  5. Khan, S., & Naseer, M. (2022). Vision Transformers for Facial Expression Recognition.
    Journal: IEEE Access.

  6. Wang, K., & Peng, X. (2021). Attention-Based CNN for Facial Emotion Recognition.
    Journal: IEEE Transactions on Multimedia.

  7. Deng, J., & Guo, J. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
    Journal: IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  8. Kollias, D., & Zafeiriou, S. (2019). A Deep Neural Network for Emotion Recognition Using CNN and RNN.
    Journal: IEEE Transactions on Affective Computing.

  9. Howard, A., & Sandler, M. (2019). MobileNetV2: Inverted Residuals and Linear Bottlenecks.
    Journal: IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  10. Ng, H. W., & Nguyen, V. D. (2018). Facial Expression Recognition Using Transfer Learning.
    Journal: IEEE International Conference on Image Processing (ICIP).

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Apr 10 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top