Face-based Photo Indexing in Edge Computing Environments

• Main Author: Pregal, Filipe Lourenço Lopes
• Publication Date: 2024
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10362/177214
• topic: Conflict-Free Replicated Data Type; Edge Computing; Indexing at the Edge; Machine Learning; Publish/Subscribe; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

Over recent years, smart mobile devices have grown in popularity. With such popularity growth, data traffic has also increased, which gave rise to new problems such as higher latency in data requests or less data storage capability. A paradigm that promises to nullify many of the issues with this growth is Edge Computing. A computing paradigm composed by the user devices, edge servers and the cloud. Edge Servers, located at the edge of the internet, close to the user’s devices, help processing and disseminating information and data. During this dissertation, we propose to enhance Chives, a machine learning API to identify faces in photos, in this environment, creating a cluster indexing system, in order to enable the development of a photo sharing application with lower latency in picture search through facial recognition. Such index is created using Conflict-free Replicated Data Types. On a mobile phone, the user will be able to search for photos with faces, using a photo of a similar face as an input. Our solution proved to retrieve the correct results, while being almost as fast as the human eye perception’s capability, being a good addition to the EdgeGarden environ- ment.