A one-dimensional non-intrusive and privacy-preserving identification system for households

Kompara, Tomaž (author), Perš, Janez (author), Susič, David (author), Gams, Matjaž (author)

In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, due to privacy concerns and regulatory restrictions, such a system should also strive to preserve the user’s privacy as much as possible. In this paper, a novel identification system is presented based on a network of laser sensors, each attached on top of the room entry. Its sensor modality, a one-dimensional depth sensor, was chosen with privacy in mind. Each sensor is mounted on the top of a doorway, facing towards the entrance, at an angle. This position allows acquiring the user’s body shape while the user is crossing the doorway, and the classification is performed by classical machine learning methods. The system is non-intrusive, non-intrusive and preserves privacy—it omits specific user-sensitive information such as activity, facial expression or clothing. No video or audio data are required. The feasibility of such a system was tested on a nearly 4000-person, publicly available database of anthropometric measurements to analyze the relationships among accuracy, measured data and number of residents, while the evaluation of the system was conducted in a real-world scenario on 18 subjects. The evaluation was performed on a closed dataset with a 10-fold cross validation and showed 98.4% accuracy for all subjects. The accuracy for groups of five subjects averaged 99.1%. These results indicate that a network of one-dimensional depth sensors is suitable for the identification task with purposes such as surveillance and intelligent ambience.

• Language: English
• Keywords: one-dimensional depth sensor, biometrics, identification, machine learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 21 str.
• Numbering: Vol. 10, iss. 5, art. 559
• PID: 20.500.12556/RUL-135023
• UDC: 004.8
• ISSN on article: 2079-9292
• DOI: 10.3390/electronics10050559
• COBISS.SI-ID: 54936323
• Publication date in RUL: 17.02.2022

Record is a part of a journal

• Title: Electronics
• Shortened title: Electronics
• Publisher: MDPI
• ISSN: 2079-9292
• COBISS.SI-ID: 523068953

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.03.2021

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0209
• Name: Umetna inteligenca in inteligentni sistemi

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0095
• Name: Vzporedni in porazdeljeni sistemi