A Cluster-Based Parallel Face Recognition System

Abstract

Abstract- The objective of content-based face recognition is to efficiently find and retrieve face images from the database that satisfy the criteria of similarity to the user's query face image. When the database is large and the face image features are complex, the exhaustive search of the database and computation of the face image similarities is not expedient. We use clusters to accelerate the face features matching speed and extend face images storage capacity. In our system, face database is partitioned into small sub-database and they are distributed among the cluster computers like disk RAID0. In this paper, we present a Double Single System Image(Middleware level and Application level) Four Tier Cluster Architecture to provide complete transparency of resource management, scalable performance, and system availability. In addition, Parallel Retrieval Virtual Machine(PRVM) data structure is designed and it improves the maintainability and extensibility of the cluster system. We also propose Multi-process, Multi-thread and Multi-ports(MMM) techniques and synchronized communication mechanism based on TCP/IP Socket to reliably implement parallel retrieval and face recognition between multi-client and multi-server. The experimental results show the cluster face recognition system not only improves the recognition speed, but also extends the data capacity of the system