deletions | additions       diff --git a/To_further_discuss_the_tracking__.tex b/To_further_discuss_the_tracking__.tex  index bfa345a..018da4d 100644  --- a/To_further_discuss_the_tracking__.tex  +++ b/To_further_discuss_the_tracking__.tex 
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To further discuss the tracking capabilities of Kinect, Automatic face analysis is introduced that incorporates face detection, face recognition, gender classification, age estimation, facial expression recognition and connected verification which has become one of the most active discussions in computer vision research \cite{jain2005handbook}. Fortunately, the newly presented low-cost depth sensors such as Microsoft Kinect that allows removing directly 3D information together with RGB color images. Detecting human face, estimating its pose and tracking it would be an essential step for application in computer vision and human–machine interaction. A significant number of research works have shown the usefulness of Kinect for face detection and segmentation \cite{masselli2012real}, head pose estimation and normalization \cite{niese2013accurate} and face tracking \cite{li2013head}. Some works \cite{fanelli2011real} make also makes  use of only depth maps while others \cite{yang2012face} combine both RGB and depth data. On the other hand, combination of color and depth data for face detection and tracking and head pose estimation is explained to accomplish more robustness than using the two modalities independently \cite{boutellaa2015use}.