Facial landmark localization by initializing the landmark points through training of local binary properties and histogram of oriented gradient

Face plays an important role in visual communication. By looking at the face, it can be automatically extracted many non-verbal messages, such as identity, intention, and emotion. In computer vision, localization of the key points of the face is usually a key step for automatic extraction of face information, and many facial analysis techniques are built on the precise recognition of these embossed. Facial landmark detection and alignment in images with occlusion is a very important and challenging task in many visual and image processing tasks. In this paper, a comprehensive method for initialization and alignment of facial landmark through training of local binary features (LBP) and histogram orientated gradient (HOG) and a facial landmark detection method using robust cascade pose regression, which are specified as pixel difference features of landmarks, is introduced. At first, by analyzing the correlation of the local binary pattern histogram (LBP) and then by using histogram orientated gradient, the features of the training images are obtained. For the test image using these features the instructional images are estimated as optimal guide points. In the test stage, according to initialization of the image, the selection of the appropriate feature for the image is used to speed up the process, which means the number of steps to be chosen for each image is better. A strong cascade mode regression is then used to adjust the face, and a local principle is applied to learn the features of the guide points. The local principle helps to learn a set of highly distinctive binary features for the face guide points independently; these local binary features are used to jointly learn the cascade mode regression for the final output. The results show that the initialization used in this work has increased the accuracy of the estimation in the cascade state regression and has obtained better results than the random initialization.