| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 普蒂雅 | zh_TW |
| DC.creator | Diyah Utami Kusumaning Putri | en_US |
| dc.date.accessioned | 2019-7-31T07:39:07Z | |
| dc.date.available | 2019-7-31T07:39:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=107522628 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 本研究提出了複雜域上的矩陣分解的新方法，以獲得提取的特徵和係數矩陣，在臉部辨識和臉部表情辨識問題中具有高識別結果。基於複數的歐拉表示將實數據矩陣變換為複數。
基礎複雜矩陣分解（CMF）使用幾個約束進行修改並在本研究中進行了研究。使用嶺項（SCMF-L2）將基本 CMF修改為稀疏複矩陣分解，其在係數中添加稀疏 L2 範數約束。本研究也開發了新穎的約束，它在稱為空間約束複雜矩陣分解（SpatialCMF）的基礎矩陣上實施像素色散懲罰。本研究構建了新的約束，它使用像素圖像表示和類註釋約束的組合來訓練稱為耦合複矩陣分解（CoupledCMF）的數據。本研究所提出的方
法與普遍大家所使用的 NMF方法和 CMF方法的擴張相比較，包括稀疏複矩陣分解（SCMF）和分別添加稀疏 L1 範數和圖形約束的圖複雜矩陣分解（GCMF）。梯度下降法則是用於解決優化問題。
臉部表情辨識場景的實驗包含整個臉部和遮蔽臉部的識別，本研究提出的方法比較普遍大家使用的 NMF和 CMF方法提升了更好的識別結果。此方法也達到停止條件，並且比 NMF和 CMF方法的擴張快得許多。 | zh_TW |
| dc.description.abstract | This work proposes novel methods of matrix factorization on the complex domain to obtain both extracted features and coefficient matrix with high recognition results in a face recognition and facial expression recognition problems. The real data matrix is transformed into a complex number based on the Euler representation of complex numbers.
The basic complex matrix factorization (CMF) is modified using several constraints and is investigated in this study. The basic CMF is modified into Sparse Complex Matrix Factorization using Ridge Term (SCMF-L2) which adds sparse L2-norm constraint in the coefficient. This study also develops novel constraint which enforces pixel dispersion penalty on the basis matrix called Spatial Constrained Complex Matrix Factorization (SpatialCMF). This study also builds novel constraint which uses the combination of pixel images representation and class annotation constraints for training data named as Coupled Complex Matrix Factorization (CoupledCMF). The proposed methods compare with prevalent NMF methods and extensions of CMF methods, including sparse complex matrix factorization (SCMF) and graph complex matrix factorization (GCMF) which adds sparse L1-norm and graph constraints, respectively. The gradient descent method is used to solve optimization problems.
Experiments on face recognition and facial expression recognition scenarios that involve a whole face and an occluded face reveal that the proposed methods provide better recognition results that common NMF and CMF methods. The proposed methods also reach the stopping condition and converge much faster than the extensions of NMF and CMF methods.
| en_US |
| DC.subject | 特徵提取 | zh_TW |
| DC.subject | 非負矩陣分解 | zh_TW |
| DC.subject | 複矩陣分解 | zh_TW |
| DC.subject | 投影梯度下降 | zh_TW |
| DC.subject | 臉部辨識 | zh_TW |
| DC.subject | 臉部表情辨識 | zh_TW |
| DC.subject | feature extraction | en_US |
| DC.subject | non-negative matrix factorization | en_US |
| DC.subject | complex matrix factorization | en_US |
| DC.subject | projected gradient descent | en_US |
| DC.subject | face recognition | en_US |
| DC.subject | facial expression recognition | en_US |
| DC.title | 臉部辨識和臉部表情辨識中實域和複域的約束矩陣分解 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Constrained Matrix Factorization in Real and Complex Domain for Face and Facial Expression Recognition | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |