Cloud-Based Face and Speech Recognition for Access Control Applications
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]
This paper describes the implementation of a system to recognize employees and visitors wanting to gain access to a physical office through face images and speech-to-text recognition. The system helps employees to unlock the entrance door via face recognition without the need of tag-keys or cards. To prevent spoofing attacks and increase security, a randomly generated code is sent to the employee, who then has to type it into the screen. On the other hand, visitors and delivery persons are provided with a speech-to-text service where they utter the name of the employee that they want to meet, and the system then sends a notification to the right employee automatically. The hardware of the system is constituted by two Raspberry Pi, a 7-inch LCD-touch display, a camera, and a sound card with a microphone and speaker. To carry out face recognition and speech-to-text conversion, the cloud-based platforms Amazon Web Services and the Google Speech-to-Text API service are used respectively. The two-step face authentication mechanism for employees provides an increased level of security and protection against spoofing attacks without the need of carrying key-tags or access cards, while disturbances by visitors or couriers are minimized by notifying their arrival to the right employee, without disturbing other co-workers by means of ring-bells.
Place, publisher, year, edition, pages
Piscataway, J.: IEEE, 2020. article id 9162165
Keywords [en]
Biometrics in the Cloud
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-42024DOI: 10.1109/cns48642.2020.9162165Scopus ID: 2-s2.0-85090112712ISBN: 9781728147604 (electronic)ISBN: 9781728147604 (print)OAI: oai:DiVA.org:hh-42024DiVA, id: diva2:1429219
Conference
6th International Workshop on Security and Privacy in the Cloud (SPC 2020), in conjunction with the eight IEEE Conference on Communications and Network Security (CNS 2020), Avignon, France, June 29-30, 2020
Funder
Swedish Research Council
Note
This work has been carried out by Nathalie Tkauc and Thao Tran in the context of their Bachelor Thesis at Halmstad University.
Authors acknowledge the CAISR program of the Swedish Knowledge Foundation.
2020-05-082020-05-082024-06-17Bibliographically approved