Drowsiness Recognition Using the Least Correlated LBPH

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Title:	Drowsiness Recognition Using the Least Correlated LBPH
Authors:	連振昌 Lien, Cheng-Chang
Contributors:	資訊工程學系 Computer Science & Information Engineering
Keywords:	瞌睡偵測;眼睛狀態;最低相關局部二值化圖樣統計直方圖(Least Correlated LPBH);獨立成分分析(ICA);支持向量機(SVM) drowsiness recognition;eye state;LC-LBPH;ICA;support vector machine
Date:	2012
Issue Date:	2014-06-27 01:36:07 (UTC+8)
Abstract:	近年來，瞌睡偵測研究廣泛應用於駕駛瞌睡偵測與遠距教學系統中，而其中眼睛狀態的辨識是建立瞌睡偵測研究裡不可或缺的基礎。然而，然而，傳統之眼睛狀態辨識很容易受光照變化或頭髮/眼鏡遮蔽的干擾。因此本計劃提出一項創新之影像特徵，稱為最低相關之LBPH紋理特徵(Least Correlated LPBH)，能夠在光照變化及穿戴眼鏡情形下正確的辨識眼睛狀態。接著將此影像紋理特徵使用獨立成分分析方法(Independent Component Analysis) 獲得低維度且具統計獨立特性之特徵向量，最後依具此新特徵向 In recent years, the drowsiness detection is widely applied to the driver alerting or distance learning. The drowsiness recognition system is constructed on the basis of the recognition of eye states. The conventional methods for recognizing the eye state
Appears in Collections:	[Department of Computer Science and Information Engineering] Seminar Papers

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