Image Training and LBPH Based Algorithm for Face Tracking in Different Background Video Sequence - Ranganatha, S; Gowramma, Y P (International Journal of Computer Sciences and Engineering, 2018)
Video and video types are changing day by day; due to which, video processing is becoming complex time to time. There is a lack of particular algorithm for automatic detection and tracking of human faces in video, to overcome the challenges that are being faced nowadays. This paper describes a model for detection and tracking of human faces in different background video sequence using OpenCV platform. Both positive and negative image samples are trained and saved as xml file. With the help of trained samples, LBPH algorithm clarifies whether the video frame contain faces or not. Further, HOG descriptor is fed to SVM detector to compute the coefficients that are stored in the xml file. Based on this, face regions are tracked until the last frame is reached. We have tested our proposed algorithm on the videos of a technically challenging dataset. Standard metrics helped to judge the success of the proposed algorithm. Test results indicate the superiority of our proposed model, compared to other similar algorithms.