A Hybrid method of face detection based on Feature Extraction using PIFR and Feature Optimization using TLBO

In this paper we proposed a face detection method based on feature selection and feature optimization. Now in\ud current research trend of biometric security used the process of feature optimization for better improvement of\ud face detection technique. Basically our face consists of three types of feature such as skin color, texture and\ud shape and size of face. The most important feature of face is skin color and texture of face. In this detection\ud technique used texture feature of face image. For the texture extraction of image face used partial feature\ud extraction function, these function is most promising shape feature analysis. For the selection of feature and\ud optimization of feature used multi-objective TLBO. TLBO algorithm is population based searching technique\ud and defines two constraints function for the process of selection and optimization. The proposed algorithm of\ud face detection based on feature selection and feature optimization process. Initially used face image data base\ud and passes through partial feature extractor function and these transform function gives a texture feature of face\ud image. For the evaluation of performance our proposed algorithm implemented in MATLAB 7.8.0 software and\ud face image used provided by Google face image database. For numerical analysis of result used hit and miss\ud ratio. Our empirical evaluation of result shows better prediction result in compression of PIFR method of face\ud detection