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Notice of Violation of IEEE Publication Principles
"Neural network Based Intelligent Local Face Recognition Using Local Pattern Averaging"
by N.Vivekanandan Reddy, D.Abhilash Krishna, P.Sharath Reddy, R.Shirisha
in the 2011 3rd International Conference on Electronics Computer Technology (ICECT), July 7, 2011, pp. 363 – 367
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Intelligent Local Face Recognition"
by Adnan Khashman
in Recent Advances in Face Recognition, Kresimir Delac, Mislav Grgic and Marian Stewart Bartlett (Ed.), InTech, 2008
This paper presents a brief review of known face recognition methods such as Principal Component Analysis (PCA) (Turk & Pentland, 1991), Linear Discriminant Analysis (LDA) (Belhumeur et al., 1997) and Locality Preserving Projections (LPP) (He et al., 2005), in addition to intelligent face recognition systems that use neural networks such as (Khashman, 2006) and (Khashman, 2007). There are many works emerging every year suggesting different methods for face recognition (Delac & Grgic, 2007); these methods are mostly appearance. This paper will also provide a detailed case study on intelligent local face recognition, where a neural network is used to identify a person upon presenting his/her face image. Local pattern averaging is used for face image preprocessing prior to training or testi- g the neural network. Averaging is a simple but efficient method that creates "fuzzy" patterns as compared to multiple "crisp" patterns, which provides the neural network with meaningful learning while reducing computational expense.