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

Author(s):  
Pushpa Choudhary ◽  
Ashish Tripathi ◽  
Arun Kumar Singh ◽  
Prem Chand Vashist

Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini

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).





2003 ◽  
Vol 13 (6) ◽  
pp. 673-678 ◽  
Author(s):  
Kyung-Ah Lee ◽  
Dae-Jong Lee ◽  
Jang-Hwan Park ◽  
Myung-Geun Chun


Author(s):  
Huiyu Zhou ◽  
Yuan Yuan ◽  
Chunmei Shi

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.



2020 ◽  
Vol 31 (3) ◽  
Author(s):  
Shigang Liu ◽  
Yuhong Wang ◽  
Yali Peng ◽  
Sujuan Hou ◽  
Keyou Zhang ◽  
...  


2017 ◽  
Vol 64 ◽  
pp. 60-83 ◽  
Author(s):  
Changhui Hu ◽  
Xiaobo Lu ◽  
Mengjun Ye ◽  
Weili Zeng


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