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Face2Feel: Emotion-Aware Adaptive User Interface

Ismail Alihan Hadimlioglu, Siddharth Linga

TL;DR

Face2Feel presents an emotion-aware adaptive UI that analyzes user expressions from video frames via DIP and face-recognition models to tailor the UI in real time. The work situates the approach among Emoticontrol and model-based adaptive UIs, supported by a case study (Shresta) and a user survey validating demand for affect-aware interfaces. It details a modular, component-based implementation, including UI customization, emotion tracking, and real-time performance optimizations to maintain responsiveness. The findings suggest broad applicability across social, educational, and professional domains, with implications for user satisfaction, accessibility, and privacy.

Abstract

This paper presents Face2Feel, a novel user interface (UI) model that dynamically adapts to user emotions and preferences captured through computer vision. This adaptive UI framework addresses the limitations of traditional static interfaces by integrating digital image processing, face recognition, and emotion detection techniques. Face2Feel analyzes user expressions utilizing a webcam or pre-installed camera as the primary data source to personalize the UI in real-time. Although dynamically changing user interfaces based on emotional states are not yet widely implemented, their advantages and the demand for such systems are evident. This research contributes to the development of emotion-aware applications, particularly in recommendation systems and feedback mechanisms. A case study, "Shresta: Emotion-Based Book Recommendation System," demonstrates the practical implementation of this framework, the technologies employed, and the system's usefulness. Furthermore, a user survey conducted after presenting the working model reveals a strong demand for such adaptive interfaces, emphasizing the importance of user satisfaction and comfort in human-computer interaction. The results showed that nearly 85.7\% of the users found these systems to be very engaging and user-friendly. This study underscores the potential for emotion-driven UI adaptation to improve user experiences across various applications.

Face2Feel: Emotion-Aware Adaptive User Interface

TL;DR

Face2Feel presents an emotion-aware adaptive UI that analyzes user expressions from video frames via DIP and face-recognition models to tailor the UI in real time. The work situates the approach among Emoticontrol and model-based adaptive UIs, supported by a case study (Shresta) and a user survey validating demand for affect-aware interfaces. It details a modular, component-based implementation, including UI customization, emotion tracking, and real-time performance optimizations to maintain responsiveness. The findings suggest broad applicability across social, educational, and professional domains, with implications for user satisfaction, accessibility, and privacy.

Abstract

This paper presents Face2Feel, a novel user interface (UI) model that dynamically adapts to user emotions and preferences captured through computer vision. This adaptive UI framework addresses the limitations of traditional static interfaces by integrating digital image processing, face recognition, and emotion detection techniques. Face2Feel analyzes user expressions utilizing a webcam or pre-installed camera as the primary data source to personalize the UI in real-time. Although dynamically changing user interfaces based on emotional states are not yet widely implemented, their advantages and the demand for such systems are evident. This research contributes to the development of emotion-aware applications, particularly in recommendation systems and feedback mechanisms. A case study, "Shresta: Emotion-Based Book Recommendation System," demonstrates the practical implementation of this framework, the technologies employed, and the system's usefulness. Furthermore, a user survey conducted after presenting the working model reveals a strong demand for such adaptive interfaces, emphasizing the importance of user satisfaction and comfort in human-computer interaction. The results showed that nearly 85.7\% of the users found these systems to be very engaging and user-friendly. This study underscores the potential for emotion-driven UI adaptation to improve user experiences across various applications.

Paper Structure

This paper contains 22 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Survey
  3. Emoticontrol: Emotions-based Control of User-Interfaces Adaptations
  4. Model-based adaptive user interface based on context and user experience evaluation
  5. Implementation
  6. User Interface
  7. Emotion detection
  8. Optimization and time complexity
  9. When Multiprocessing and Optimization Are Applied
  10. Without Multiprocessing
  11. Comparison and Conclusion
  12. Case Study
  13. Introducing "Shresta: Emotion-based Book recommendation system"
  14. System Architecture and Implementation
  15. Frontend
  16. ...and 7 more sections

Figures (8)

  • Figure 1: working of DIP and emotion detection
  • Figure 2: Glimpse of the system
  • Figure 3: System Architecture for Emotion Detection using Webcam, OpenCV, and DeepFace.
  • Figure 4: UI changes when user is Sad
  • Figure 5: UI chenages when the user is Angry
  • ...and 3 more figures