Face Recognition From Video Using Enhanced Social Collie Optimization-based Deep Convolutional Neural Network

The face recognition system is a process which is part of computer vision as a key feature of video surveillance systems. In a face recognition system stands on two main pillar which are object recognition and authentication of object or person who present in a frame with the help of distinctive picture generation like i.e. Cameras. In this field of Computer Vision many researchers work on detection and verification of faces from frames in the subsequent part of video. It is a complex and complicated process to recognize accurate faces by the use of artificial vision algorithms. In machine learning main important concept is Convolutional neural networks (CNNs) in real time system which has video process input for face recognition systems. For such complex problem effective approaches to work on by solution as CNNs have excelled capability to handle, with significant results. Which also including process based voice recognition, face localization, face recognition and picture frame selection and categorization. Recently all the top performing techniques which work on the preprocessed Labelled Face dataset for the Real Time system for convolutional neural networks (CNNs).So here in this research use of deep convolutional neural network (optimized Deep CNN) to getting generated precise outcome of face images from the input video by the Hybrid weighted texture pattern descriptor (HWTP). The help of deep CNN techniques with improved tuned featured extraction and introduce novel ideas about enhanced social collie optimization (ESCO), which result in better solutions with different combinations of strategies. The finest parameters are used to recognise the face of an individual is determined. The proposed model by deep convolutional neural network achieves 91.30% accuracy, 95.07% precision, 95.078% recall and 95.078% f-measures.