Published In : IJCSN Journal Volume 5, Issue 6

Date of Publication : December 2016

Pages : 948-954

Figures :01

Tables : 01

 

Isha Pandya : was born in Nagpur, India in 1992. She received the BE degree in Computer Science and Engineering from G.H.Raisoni college of Engineering in 2014. She is currently pursuing M.Tech in Computer Science and Engineering from G.H.Raisoni college of Engineering, Nagpur, India. Her research interest includes image processing.

Deepti Theng : received her BE and MTech in Computer Science and Engineering in 2007 and 2012 respectively. She is currently working as an Assistant Professor in the Department of Computer Science and Engineering, GHRCE, Nagpur. Her current research interests include Cloud Computing, High Performance Computing, Parallel and Distributed Computing. She has more than 55 National and International papers published including publications of IEEE, Elsevier, Springer and many more. She is an active Professional Member of IEEE, SMC, ACM, and CSI. She has been actively involved in many International conferences, journals as Technical Program Committee Member and on Technical Board.

 

 

 

 

 

 

 

Classification, Expression Recognition, Facial Expression Detection, Face Detection, Face Tracking, Feature Extraction

In this paper, various techniques of face tracking and detection, feature extraction, feature comparing and classification are reviewed. The best suited algorithms in each of the modules can be in the system. The steps for facial expression recognition to detect a lie of a person are shown. One can use CAM-Shift algorithm to detect and track faces in a video frame if importance is given on memory requirement and time. It is also invarient to rotation. PCA is good for smaller dimensions, is low noise sensitivity, and less memory requirement. SVM is the most popular method for classification. It is suited for two-class problem. Choosing any algorithm is based on the requirement of your system and it totally depends on the system developer.

 

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