Accurate emotion recognition from human-computer interactions is essential for adaptive and user-centered applications. Traditional affect recognition methods often overlook individual behavioral patterns, thereby limiting their effectiveness across diverse user populations. We propose a personalized deep learning framework for emotion recognition from keystroke dynamics that addresses this limitation.
Our dual-input neural network architecture combines temporal features extracted from keystroke synchronization patterns and user-specific metadata via Feature-wise Linear Modulation (FiLM) and Low-Rank Adaptation (LoRA). This design enables the model to capture both universal typing patterns and individual-specific behavioral characteristics. Trained and evaluated on the EmoSurv dataset, the proposed model achieves around 98% accuracy across five emotion classes: neutral, happy, sad, calm, and angry.
Experimental results demonstrate that the personalized dual-input architecture with FiLM and LoRA conditioning improves performance compared to user-agnostic baselines. Per-class analysis further indicates more consistent recognition across emotions. By employing parameter-efficient FiLM and LoRA modules for user-specific conditioning, the framework introduces personalization while maintaining a compact model design, supporting its applicability to real-time keystroke-based emotion recognition scenarios.
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (RS-2023-00256629) grant funded by the Korea government (MSIT), and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2026-RS-2024-00437718) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation). Department of AI Convergence, Chonnam National University, Gwangju, Republic of Korea Karina Kolmogortseva, Soo-Hyung Kim, Hyung-Jeong Yang & Seung-Won Kim School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7001, Australia The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Kolmogortseva, K., Kim, SH., Yeom, S. et al.
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