3. RELATED WORK
David Pintor Maestreet al. [1] developed an authentication method
using two factor authentication and QR code to improve the data
security. Dong-Sik oh et al. [2] projected three set of QR codes for
converting the single information into three versions of QR code and
stored in distributed server system. Peter Kieseberg, Manuel Leithner,
Martin Mulazzani, Lindsay Munroe, Sebastian [3] described about QR
security. Suraj Kumar Sahu et al. [4] described an encryption
procedure by embedding the QR code for stegnograpy.
An image of the iris was considered as an input to the GLCM and
super-resolute algorithms by AnandDeshpande, Prashant, PatavardhanRao
[5]. Benazir.K.KVijayakumar [6] used GLCM band fingerprint
feature extraction. Dubey, S. R., S. K. Singh, and R. K. Singh [7],
Content-based image retrieval (CBIR) is demands accuracy with efficient
retrieval approaches to index and retrieve the most similar images from
the huge image databases. Fei Wang, Jingdong Wang, Changshui Zhang,
James Kwok [8] proposed special feature analysis. Lifang Wu
XingshengLiu, Songlong Yuan, Peng [9] proposed a biometric
cryptosystem based on face biometrics. ManishaLumb Research Scholar,
D.A.V.I.E.T, Jalandhar Poonam [10] concluded that HSV and dithered
images can be extracted first than RGB and YIQ. Mary.EShyla and
Punithavalli [11] have used feature identification model in their
work and have developed a technique named Color Component Feature
Identification using the Bayes Classifier. Mohammed Tajuddin [12]
proposed an innovative human biometric from retinal blood vessels as a
key which is not stored in the database. This resulted in increased
network security. P. Mohanaiah , P. Sathyanarayana , L. GuruKumar
[13] used GLCM to extract texture features such as angular second
moment, correlation, inverse difference moment and entropy. Nitish
Zulpe1 and Vrushsen [14] took Magnetic Resonance Imaging (MRI) as an
input to the GLCM. Preprocessing of the MRI image of brain tumor is done
using GLCM with Levenberg Marquart (LM) Nonlinear optimization
algorithm. Santhi, Ravichandran, Arun and Chakkarapani [15] used the
Gray Level Co-occurrence matrix of an image to extract the Gray Level
Co-occurrence properties of the image Selvarani and Malarvizhi [16]
used fingerprint and Iris to find a key.
Tawfiq Barhoom Zakaria, Abusilmiyeh [17] proposes a method for
encrypting the sender’s messages using new algorithm with a secret key
which is generated from using color image and the difference in the LSB
of the image pixels. Abdul Rehman Khan, Nitin Rakesh and
Rakesh MatamShailesh Tiwari [18] describe the elements that are
vital for feature extraction process from a Grey Level Co-occurrence
Matrix. Every pattern recognition model consists of a primary phase
where Sabanozturk, Bayramakdemir [19] determined the most successful
feature extraction classification algorithm for histopathological
images. Than
ThanHtay and Su
SuMaung [20] describe through his studies feature extraction is
focused on the first order statistical and Gray Level Co-occurrence
Matrix (GLCM) based textural features extraction techniques.