Deep Facial Recognition by Feature and Pose Analysis through Facial Key markers

Abstract

The Facial key markers are all inclusive of centers and cereal of the eyes, eyebrows, nose and mouth, among the facial features. Our epistemology concern four track to produce our yield phraseology. The first step regards a binary facial feature aggregation augmentation method to detect the facial features and processed through the vgg16 network these facial features are stored as network file for the deep features recognition in a group images that are to be given as an input to the recognition process. The algorithm learns at significant pace for an accurate recognition. For the second phase the paper handled a definite quantity of convolution neural networks the pre-trained network file, with the top-quality discipline derivable from quondam VGG 16 Net models. At test case, the paper discourse our conceptualization of once more using information network recognition method to bring forth a complex of images recognition from a individual trial network file with facial mapping processed in the previous step, allowing for mean enunciation. Lastly, our model utilizes leaden dramatis derived from the network file to match personae of models to convey our concluding Key marker embeddings on the certain image. The test phase undergoes through the input image with multiple face present in it, the network (vgg16) file analyzes the faces in the image and recognizes the faces in the first instance. The n the faces will accurately marked with facial key marker for each region of the face. The faces were named in the training process into a supporting file format to be named. These names are then assigned to the faces accurately. We reference our differential model bailiwick, with and without information diminution method based on their mean squared root of the results they bring forth on the test methodology, The test accuracy of facial recognition with facial key markers are found to 96%.

Keywords: VGG16, Key markers, facial features, CNN & face recognition.