Analysis of Facial Expression and Recognition Based on Statistical Approach
Renuka R. Londhe1, Vrushshen P. Pawar2
1Renuka R. Londhe, College Of Computer Science and IT, SRTM University, Nanded, India.
2Dr. Vrushshen V. Pawar, Department of computational studies, Swami Ramanand Teerth Marathwada University, Nanded, India.
Manuscript received on April 11, 2012. | Revised Manuscript received on April 14, 2012. | Manuscript published on May 05, 2012. | PP: 391-394 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0624042212/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Facial Expression Recognition is rapidly becoming area of interest in computer science and human computer interaction. The most expressive way of displaying the emotions by human is through the facial expressions. In this paper, Recognition of facial expression is studied with the help of several properties associated with the face itself. As facial expression changes, the curvatures on the face and properties of the objects such as, eyebrows, nose, lips and mouth area changes. Similarly, intensity of corresponding pixels of images also changes. We have used statistical parameters to compute these changes and computed results (changes) are recorded as feature vectors. Artificial neural network is used to classify these features in to six universal expressions such as anger, disgust, fear, happy, sad and surprise. Two-layered feed forward neural network is trained and tested using Scaled Conjugate Gradient back-propagation algorithm and we obtain 92.2 % recognition rate.
Keywords: Facial expression Recognition, Human Computer Interaction, Scaled Conjugate Gradient, Statistical parameters.