deletions | additions       diff --git a/To_further_discuss_the_tracking__.tex b/To_further_discuss_the_tracking__.tex  index 28c86fa..856b55b 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,When it comes to  Automatic face analysis which incorporates, e.g. 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, information  together with RGB color images. Detecting human face, estimating its pose and tracking it would be an essential steps step  for many applications application  in computer vision research  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 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}. $a + dt$