International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org

Call for Paper, Published Articles, Indexing Infromation Facial Semantics Recognition Method for Content based Video Retrieval Systems, Authors : B. S. Daga, A. A. Ghatol ,V.M.Thakare, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), www.ijettcs.org
Volume & Issue no: Volume 6, Issue 1, January - February 2017

Title:
Facial Semantics Recognition Method for Content based Video Retrieval Systems
Author Name:
B. S. Daga, A. A. Ghatol ,V.M.Thakare
Abstract:
Abstract With growing video databases, the accurate facial expression identifier systems are proving their importance. State of the art literature suggests the use of facial texture pattern while identifying the facial expression from a image frame or photograph, and exercise with moving geometric patterns while dealing with dynamics expression identification from a video. Accordingly they make use of local binary patterns (LBP) from a image frame or facial landmark tacking (FLT) to extract out the semantics from a video database. Actual classification and identification of expression is then performed by support vector machine (SVM) based classifier. This work primarily presents a comparative assessment of LBP and FLT methods as semantic means for video retrieval systems. Moreover herein introduces possible implication of probabilistic neural network (PNN) as more effective alternative to conventionally practiced SVM for classification problem. The modular system so developed has been tested with well-established database and comparative results of the methods are presented. Results presented indicate that the implication of order of magnitude faster PNN can be efficient replacement of SVM. Moreover, considering the near future technologies, use of facial landmark tracking technique is most viable solution to yield accurate and meaningful results. The facial expression identification based on different facial semantics has been one of well-researched areas for quite some time. However, expecting a proven system implication into real practice needs to focus on systems performance and processing speed. The usefulness of current work lies in its contribution towards presenting a comparative study of classifiers for content based video retrieval systems. Keywords: Facial Landmark Tracking, Support Vector Machine, Neural Network, Semantic, Facial Expressions, Content Based Video Retrieval System
Cite this article:
B. S. Daga, A. A. Ghatol ,V.M.Thakare , " Facial Semantics Recognition Method for Content based Video Retrieval Systems " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 6, Issue 1, January - February 2017 , pp. 139-146 , ISSN 2278-6856.
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