Multi-modal Open World User Identification
Bibliographic Data
| ID | 22190855 |
|---|---|
| Authors | Bahar Irfan (0000-0002-7983-079X, University of Plymouth), M Ortiz (0000-0001-5290-7403, City, University of London), Michael Garcia Ortiz (AI Lab, SoftBank Robotics Europe and City, University of London, London, United Kingdom), Natalia Lyubova (Laboratoire de Recherche sur la Croissance Cellulaire, la Réparation et la Régénération Tissulaires), Tony Belpaeme (0000-0001-5207-7745, University of Plymouth) |
| Year | 2022 |
| Volume | 11 |
| Issue | 1 |
| Pages | 1-50 |
| Publication date | 2022-03-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Journal identifiers | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Publisher | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3477963 |
| OpenAlex | W3205054974 |
| Language | EN |
| Citations received | 2 |
| References cited | 69 |
User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world
Biometrics · Facial recognition system · Machine learning · Modal · Computer Science · Domain Adaptation and Few-Shot Learning · Face recognition and analysis · Social Robot Interaction and HRI · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2024 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |