Facial Feature Detection Algorithm Based on Main Characteristics of Eyes

Chang Yuan Wang; Mei Juan Qu; Hong Bo Jia; Hong Zhe Bi

doi:10.4028/www.scientific.net/AMM.303-306.1402

Paper Titles

A Virtual Sample Generation Approach for Blast Furnace Fault Diagnosis
p.1379

An Architecture for Home Service Retrieval Based on Function Concept Ontology
p.1385

An Intelligent Job Scheduling System in Cloud Computing
p.1391

Application Effect of Aquaculture IOT System
p.1395

Facial Feature Detection Algorithm Based on Main Characteristics of Eyes
p.1402

Image Retrieval Method Based on Vision Feature of Color
p.1406

Intelligent Petrol Station Recommender Based on Context Ontology and Rule Inference
p.1412

Investigation of Emergency Resources Models Based on Ontology
p.1416

Mobile Geographic Web Search Personalization with Language Model
p.1420

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Facial Feature Detection Algorithm Based on Main...

Facial Feature Detection Algorithm Based on Main Characteristics of Eyes

Abstract:

This paper proposed a new facial feature points localization algorithm based on main characteristics of eyes.Use the result of pupil center position to initialize the model of hybrid improved active shape model (ASM) and active appearance model (AAM). The algorithm will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it optimizes measure functions of ASM and AAM to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

You might also be interested in these eBooks

Sensors, Measurement and Intelligent Materials

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

1402-1405

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.1402

Citation:

Cite this paper

Online since:

February 2013

Authors:

Chang Yuan Wang, Mei Juan Qu, Hong Bo Jia, Hong Zhe Bi

Keywords:

Active Appearance Model (AAM), Active Shape Model (ASM), Circular Hough Transform (CHT), Facial Feature Extraction, Feature Location, Pupil Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Changsheng Zhu, Zhiliang Wang. Journal of Chinese Computer Systems. Vol. 30(2009), p.1155(In Chinese).

Google Scholar

[2] Haina Song, Gangyao Kuang, etc. ActaElectronica Sinica. Vol. 31(2003), pp.1433-36(In Chinese).

Google Scholar

[3] Jinye Peng, Bianzhang Yu, etc. Acta Electronica Sinica. Vol. 30(2002), pp.363-366(In Chinese).

Google Scholar

[4] Junying Gan, Youwei Zhang, etc. Acta Electronica Sinice. Vol. 30(2002), pp.1013-16(In Chinese).

Google Scholar

[5] T.F. Cootes, C.J. Taylor. Computer Vision and Image Understanding. Vol. 61(1995), pp.38-59.

Google Scholar

[6] T.F. Cootes, C.J. Taylor, and J. Halsam. Images and Vision Computing. Vol. 12(1994), pp.355-366.

Google Scholar

[7] T.F. Cootes, C.J. Taylor. Statistical Models of Appearance for Computer Vision R. Wolfson Image Analysis Unit. University of Manchester 84-86(2002).

Google Scholar

[8] Xing Niu, Zhihong Xi, MASAHIDE Kaneko. Face feature points extraction based on refined AAM[J]. Applied Science and Technology, 2011, 38(4): 35-38. (In Chinese).

Google Scholar

[9] Zhihui Lei, Qifeng Yu. A new method to determine eye movement and translational[J]. Experimental Mechanics, 2003, 18(4): 564-568(In Chinese).

Google Scholar