A robust incremental clustering-based facial feature tracking |
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| Affiliation: | 1. Department of Computer Science & Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh;2. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), Malaysia;3. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;1. Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran;2. Systems Engineering Group, Department of Engineering & Technology, University of Huddersfield, UK;1. Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;2. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;3. Vietnam-German University (VGU), Vietnam;4. Department of Mechanical Engineering, Incheon National University, South Korea;1. DInf – Federal University of Parana, CP: 19081, CEP 19031-970, Curitiba, Brazil;2. Intelligent System Group, University of the Basque Country, San Sebastian, Spain;1. Dept. of Computer Eng. & Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt;2. Dept. of Computer Science, Faculty of Computers, Mansoura University, Mansoura, Egypt;1. Departamento de Informática y Estadística, Universidad Rey Juan Carlos, Móstoles, Spain;2. Departamento Estadística e Investigación Operativa, Universidad de Valencia, Burjassot, Spain;3. Lab-STICC, Centre de Recherche, Université de Bretagne-Sud, Lorient, France;4. LERIA, Département d’informatique, Université d’Angers, Angers, France;1. Institute of Computer Science, Siedlce University of Natural Sciences and Humanities, 3-Maja 54, 08-110 Siedlce, Poland;2. Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, 01-248 Warsaw, Poland;3. Faculty of Mathematics and Computer Science, Lodz University, Banacha 22, 90-238 Lodz, Poland |
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| Abstract: | Emerging significance of person-independent, emotion specific facial feature tracking has been actively tracked in the machine vision society for decades. Among distinct methods, the Constrained Local Model (CLM) has shown significant results in person-independent feature tracking. In this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. A novel tracking system along with 17-point feature model on the frontal face region has also been proposed to facilitate the tracking of human basic facial expressions. The proposed feature tracking system keeps patch images and face shapes till certain number of key frames incorporating CLM-based tracker. After that, incremental patch and shape clustering algorithms is applied to build appearance model and structure model of similar patches and similar shapes respectively. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed Robust Incremental Clustering-based Facial Feature Tracking (RICFFT) is evaluated on the FGnet database and the Extended Cohn-Kanade (CK+) database. RICFFT demonstrates mean tracking accuracy of 97.45% and 96.64% for FGnet and CK+ database respectively. Also, RICFFT is more robust by minimizing average shape distortion error of 0.20% and 1.86% for FGnet and CK+ (apex frame) database, as compared with classic method CLM. |
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| Keywords: | Facial feature tracking Incremental clustering Feature tracking framework Facial feature model Constrained local model |
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