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Face recognition and speech recognition for access control
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2019 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This project is a collaboration with the company JayWay in Halmstad. In order to enter theoffice today, a tag-key is needed for the employees and a doorbell for the guests. If someonerings the doorbell, someone on the inside has to open the door manually which is consideredas a disturbance during work time. The purpose with the project is to minimize thedisturbances in the office. The goal with the project is to develop a system that uses facerecognition and speech-to-text to control the lock system for the entrance door.

The components used for the project are two Raspberry Pi’s, a 7 inch LCD-touch display, aRaspberry Pi Camera Module V2, a external sound card, a microphone and speaker. Thewhole project was written in Python and the platform used was Amazon Web Services (AWS)for storage and the face recognition while speech-to-text was provided by Google.The system is divided in three functions for employees, guests and deliveries. The employeefunction has two authentication steps, the face recognition and a random generated code that

needs to be confirmed to avoid biometric spoofing. The guest function includes the speech-to-text service to state an employee's name that the guest wants to meet and the employee is then

notified. The delivery function informs the specific persons in the office that are responsiblefor the deliveries by sending a notification.The test proves that the system will always match with the right person when using the facerecognition. It also shows what the threshold for the face recognition can be set to, to makesure that only authorized people enters the office.Using the two steps authentication, the face recognition and the code makes the system secureand protects the system against spoofing. One downside is that it is an extra step that takestime. The speech-to-text is set to swedish and works quite well for swedish-speaking persons.However, for a multicultural company it can be hard to use the speech-to-text service. It canalso be hard for the service to listen and translate if there is a lot of background noise or ifseveral people speak at the same time.

Place, publisher, year, edition, pages
2019. , p. 45
Keywords [en]
Face recognition, speech recognition
National Category
Other Engineering and Technologies Human Computer Interaction
Identifiers
URN: urn:nbn:se:hh:diva-39776OAI: oai:DiVA.org:hh-39776DiVA, id: diva2:1325334
External cooperation
JayWay
Subject / course
Computer science and engineering
Educational program
Computer Engineer, 180 credits
Supervisors
Examiners
Available from: 2019-06-18 Created: 2019-06-15 Last updated: 2019-06-25Bibliographically approved

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Citation style
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