Selection and Efficient Use of Local Features for Face and Facial Expression Recognition in a Cortical Architecture

Matsugu, Masakazu

doi:10.5772/4843

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Cited by 2 publications

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“…In this work, an intelligent local face recognition system which considers a person's essential face features (eyes, nose and mouth) will be presented, and suggestions are made that a person's face can be recognized regardless of his/her facial expression whether being smiley, sad, surprised…etc. Previous works successfully used local facial features for face recognition purposes (Campadelli et al, 2007), (Matsugu, 2007); and also for recognising the facial expression of a person (Matsugu, 2007), (Pantic & Bartlett, 2007). The chapter is organized as follows: section 1 contains an introduction to the chapter.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Local Face Recognition

Khashman¹

2008

Recent Advances in Face Recognition

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Section: Introductionmentioning

confidence: 99%

Intelligent Local Face Recognition

Khashman¹

2008

Recent Advances in Face Recognition

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Notice of Violation of IEEE Publication Principles - Neural network based intelligent local face recognition using local pattern averaging

Reddy¹,

Krishna²,

Reddy³

et al. 2011

2011 3rd International Conference on Electronics Computer Technology

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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 testing 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.

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Selection and Efficient Use of Local Features for Face and Facial Expression Recognition in a Cortical Architecture

Cited by 2 publications

References 37 publications

Intelligent Local Face Recognition

Intelligent Local Face Recognition

Notice of Violation of IEEE Publication Principles - Neural network based intelligent local face recognition using local pattern averaging

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