A part-based rotational invariant hand detection

Author

Jisu Kim ; Jeonghyun Baek ; Euntai Kim

Author_Institution

Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea

fYear

2013

fDate

6-8 Dec. 2013

Firstpage

127

Lastpage

129

Abstract

This paper proposes part-based rotational invariant hand detection in complicated environment. The proposed method consists with 4 steps: head detection, back projection, hand rotation and hand detection. To improve the hand detection performance, the human head information is used for generating effective hand ROIs. Generated hand ROIs are verified using Histogram of Oriented (HOG) feature and Support Vector Machine (SVM) classifier. In the experiment, the proposed method is compared with previous methods and detection performance and computational time are evaluated.

Keywords

computer vision; feature extraction; gesture recognition; image classification; object detection; support vector machines; HOG; SVM; back projection; computer vision; hand ROI generation; hand detection performance improvement; hand gesture detection; hand rotation; head detection; histogram-of-oriented feature; human head information; part-based rotational invariant hand detection; region-of-interest; support vector machine classifier; Educational institutions; Feature extraction; Head; Image color analysis; Robustness; Skin; Support vector machines; HOG feature; Rotational invariant hand detection; SVM classifier;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Theory and Its Applications (iFUZZY), 2013 International Conference on

Conference_Location

Taipei

Type

conf

DOI

10.1109/iFuzzy.2013.6825422

Filename

6825422