الفهرس 
Automatic image tagging using facial recognition
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Abstract
With the popularity of digital cameras, recent years have witnessed a rapid growth of capturing selfie personal images. People capture self-images to record their lives and share them on the web. Face recognition has been one of the most interesting and important approach fields in the past two decades. The reasons come from the need of automatic recognitions systems. Different approaches have been published to overcome different factors (such as illumination, facial expression, scale, pose, {u2026}{u2026}) and achieve better recognition rate. However, there is still room for improvement. In this thesis, face recognition method is being applied to identify human faces using particle swarm optimization (PSO) to optimize Hidden markov model (HMM) states and parameter of face recognition system. Near optimal feature selection for the face images based on the idea of collaborative behavior of bird flocking (PSO) is used to reduce feature size and recognition time. The system examines 400 face images of the olivetti research Laboratory (ORL) face database and 400 face images of the Facebook images database, with a recognition rate of 98.5% and 90% respectively