Upper Half Face Recognition Using Hidden Markov Model and Singular Value Decomposition Coefficients

2020 ◽  
pp. 549-561
Author(s):  
Pushpa Choudhary ◽  
Ashish Tripathi ◽  
Arun Kumar Singh ◽  
Prem Chand Vashist
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

2019 ◽  
Vol 9 (2) ◽  
pp. 19-26
Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Parameter Determination ◽  
Iris Image ◽  
Determination System ◽  
Value Decomposition

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).

Face recognition in constrained environment using hidden Markov model approach

2009 ◽  
Author(s):  
P. Shah ◽  
S. Kar ◽  
S. Hiremath ◽  
D.G. Joshi ◽  
V.K. Chadda
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Approach

scholarly journals Face Recognition Using Wavelet Coefficients and Hidden Markov Model

2003 ◽  
Vol 13 (6) ◽  
pp. 673-678 ◽  
Author(s):  
Kyung-Ah Lee ◽  
Dae-Jong Lee ◽  
Jang-Hwan Park ◽  
Myung-Geun Chun
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Wavelet Coefficients

Face Recognition and Semantic Features

2011 ◽  
pp. 80-98
Author(s):  
Huiyu Zhou ◽  
Yuan Yuan ◽  
Chunmei Shi
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Eigenvalue Problem ◽  
Singular Value ◽  
Semantic Features ◽  
Subspace Analysis ◽  
Geometrical Properties ◽  
The Face ◽  
Tensor Subspace ◽  
Value Decomposition

The authors present a face recognition scheme based on semantic features’ extraction from faces and tensor subspace analysis. These semantic features consist of eyes and mouth, plus the region outlined by three weight centres of the edges of these features. The extracted features are compared over images in tensor subspace domain. Singular value decomposition is used to solve the eigenvalue problem and to project the geometrical properties to the face manifold. They compare the performance of the proposed scheme with that of other established techniques, where the results demonstrate the superiority of the proposed method.

Singular value decomposition-based virtual representation for face recognition

2020 ◽  
Vol 31 (3) ◽  
Author(s):  
Shigang Liu ◽  
Yuhong Wang ◽  
Yali Peng ◽  
Sujuan Hou ◽  
Keyou Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Singular value decomposition and local near neighbors for face recognition under varying illumination

2017 ◽  
Vol 64 ◽  
pp. 60-83 ◽  
Author(s):  
Changhui Hu ◽  
Xiaobo Lu ◽  
Mengjun Ye ◽  
Weili Zeng
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition
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