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Framework for a Dynamic Grid-Based Surveillance Face Recognition System

Face recognition has in recent times achieved an onward approval from people compared to other biometric systems, this is because of its many application areas such as authentication systems, security systems, surveillance systems etc among others. More so, recognizing faces poses as a major form of daily human social intercourse. However, existing face recognition systems are computationally intensive such that their performance under heavy load as applicable under systems like large scale surveillance systems where real-time operation is required is questionable. This work presents a novel framework for face recognition where real-time recognition is required on a large scale; the approach adopted is multi-component autonomic grid-based computing which gives a scalable system. The framework introduces separation of the system into different functional component such as the main recognition engine, live-scene capture engine, distributed data repository and the notifying subsystem which all works together to achieve a common goal. For the face recognition engine, principal component analysis is combined with a feature based technique. This paper describes the proposed architecture in detail.