Selection of Facial Geometric Key Points using Convolution Neural Network

Face Recognition is a significant authentication procedure that is being used in bio-metrics in these days and age. Face recognition is based on various algorithmic approaches. Deep learning-based neural networks have a significant influence on the facial recognition approach. VGG (Visual Geometric Graphic) based networks are the multiple layered convNet is a 224*224 RGB channel based facial network built on various face samples. The major feature of this paper is the development of the facial key markers from facial features the procedure is conducted on samples of various data sets. The samples are then processed into the VGG19-based neural network for the facial detection of the samples processed from the data sets. Then the facial key features are extracted from the faces that are processed from the samples. The facial key features are based on the faces' eyes, forehead, and lip regions. Then the facial key markers are identified on the various faces. Then the facial geometry is calculated based on the facial key markers that are located on the individual faces the calculated facial geometry is then applied to the facial recognition of the sample of the data sets. This facial geometry is the basis of deep facial recognition on the facial samples. The face recognition that is conducted has an accuracy of 98%. Facial recognition is the basis of various authentication procedures for web-based login. The developed methodology is a low-cost computational procedure to conduct accurate facial recognition on commodity hardware.