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.