Expression Recognition using Cascade Local Deformation Code
碩士 === 國立臺灣科技大學 === 機械工程系 === 100 === An appearance-based coding scheme, called Cascade Local Deformation Code (CLDC), is proposed for expression recognition. CLDC has two component codes, Human Observable Code (HOC) and Haar-like Feature Code (HFC). The HOC encodes the local deformation regions cau...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2012
|
Online Access: | http://ndltd.ncl.edu.tw/handle/836p6p |
id |
ndltd-TW-100NTUS5489176 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-100NTUS54891762019-05-15T20:51:12Z http://ndltd.ncl.edu.tw/handle/836p6p Expression Recognition using Cascade Local Deformation Code 聯結式局部特徵編碼之表情辨識 Yu-Shan Lin 林鈺山 碩士 國立臺灣科技大學 機械工程系 100 An appearance-based coding scheme, called Cascade Local Deformation Code (CLDC), is proposed for expression recognition. CLDC has two component codes, Human Observable Code (HOC) and Haar-like Feature Code (HFC). The HOC encodes the local deformation regions caused by facial muscle contractions observable to humans, and the HFC encodes the Haar-like features selected by an AdaBoost algorithm. Given a training set, one first selects the observable local deformation regions, and trains a HOC detector which encodes the local deformation regions into HOC codewords according to seven predefined expressions. The training set is also used for the extraction of Haar-like features and encoding of the features into HFC codewords for the seven expressions. The combination of HOC and HFC gives the CLDC, which is proven to outperform either component in the decoding phase for the expression recognition on disjoint testing sets. Experiments on the CK+, JAFFE and the latest FERA databases show that the performance of the CLDC is competitive to the state-of-the-art approaches. Gee-Sern Hsu 徐繼聖 2012 學位論文 ; thesis 79 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立臺灣科技大學 === 機械工程系 === 100 === An appearance-based coding scheme, called Cascade Local Deformation Code (CLDC), is proposed for expression recognition. CLDC has two component codes, Human Observable Code (HOC) and Haar-like Feature Code (HFC). The HOC encodes the local deformation regions caused by facial muscle contractions observable to humans, and the HFC encodes the Haar-like features selected by an AdaBoost algorithm. Given a training set, one first selects the observable local deformation regions, and trains a HOC detector which encodes the local deformation regions into HOC codewords according to seven predefined expressions. The training set is also used for the extraction of Haar-like features and encoding of the features into HFC codewords for the seven expressions. The combination of HOC and HFC gives the CLDC, which is proven to outperform either component in the decoding phase for the expression recognition on disjoint testing sets. Experiments on the CK+, JAFFE and the latest FERA databases show that the performance of the CLDC is competitive to the state-of-the-art approaches.
|
author2 |
Gee-Sern Hsu |
author_facet |
Gee-Sern Hsu Yu-Shan Lin 林鈺山 |
author |
Yu-Shan Lin 林鈺山 |
spellingShingle |
Yu-Shan Lin 林鈺山 Expression Recognition using Cascade Local Deformation Code |
author_sort |
Yu-Shan Lin |
title |
Expression Recognition using Cascade Local Deformation Code |
title_short |
Expression Recognition using Cascade Local Deformation Code |
title_full |
Expression Recognition using Cascade Local Deformation Code |
title_fullStr |
Expression Recognition using Cascade Local Deformation Code |
title_full_unstemmed |
Expression Recognition using Cascade Local Deformation Code |
title_sort |
expression recognition using cascade local deformation code |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/836p6p |
work_keys_str_mv |
AT yushanlin expressionrecognitionusingcascadelocaldeformationcode AT línyùshān expressionrecognitionusingcascadelocaldeformationcode AT yushanlin liánjiéshìjúbùtèzhēngbiānmǎzhībiǎoqíngbiànshí AT línyùshān liánjiéshìjúbùtèzhēngbiānmǎzhībiǎoqíngbiànshí |
_version_ |
1719104769847459840 |