✦ LIBER ✦

Probabilistic two-dimensional principal component analysis and its mixture model for face recognition

✍ Scribed by Haixian Wang; Sibao Chen; Zilan Hu; Bin Luo

Publisher: Springer-Verlag
Year: 2007
Tongue: English
Weight: 364 KB
Volume: 17
Category: Article
ISSN: 0941-0643
DOI: 10.1007/s00521-007-0155-1

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Structural two-dimensional principal com

Structural two-dimensional principal component analysis for image recognition

✍ Haixian Wang 📂 Article 📅 2009 🏛 Springer-Verlag 🌐 English ⚖ 391 KB

Generalized 2D principal component analy

Generalized 2D principal component analysis for face image representation and recognition

✍ Hui Kong; Lei Wang; Eam Khwang Teoh; Xuchun Li; Jian-Gang Wang; Ronda Venkateswa 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 294 KB

Two-dimensional canonical correlation an

Two-dimensional canonical correlation analysis and its application in small sample size face recognition

✍ Ning Sun; Zhen-hai Ji; Cai-rong Zou; Li Zhao 📂 Article 📅 2009 🏛 Springer-Verlag 🌐 English ⚖ 371 KB

Improved discriminate analysis for high-

Improved discriminate analysis for high-dimensional data and its application to face recognition

✍ Xiao-Sheng Zhuang; Dao-Qing Dai 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 672 KB

Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component analysis (PCA) is a common used dimension reduction technique. Linear discriminate analysis (LDA) is often employed for classification. PCA plus LDA is a famous

Visual learning and recognition of 3D ob

Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach

✍ P. Nagabhushan; D.S. Guru; B.H. Shekar 📂 Article 📅 2006 🏛 Elsevier Science 🌐 English ⚖ 478 KB