Local Gabor Fisher Classifier for Face Recognition

Huang, Hao; Zhou, Fan

doi:10.1109/icig.2007.76

Cited by 3 publications

(1 citation statement)

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“…Many classic methods such as principal component analysis (PCA) [1] , independent component analysis (ICA) [2] , fisher's linear discriminant (FLD) [3] and local feature analysis (LFA) have been applied to facial expression recognition. Hidden Markov model, as a primitive statistical theory model of system event series, also has been applied to facial expression recognition.…”

Section: Introductionmentioning

confidence: 99%

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

Zhan

Cheng

Chen

et al. 2010

J. Comput. Sci. Technol.

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Zhan YZ, Cheng KY, Chen YB et al. A new classifier for facial expression recognition: Fuzzy buried Markov model. Abstract To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information (MMI) method to replace maximum likelihood (ML) method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.

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Section: Introductionmentioning

confidence: 99%

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

Zhan

Cheng

Chen

et al. 2010

J. Comput. Sci. Technol.

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One-shot facial feature extraction based on Gauss-Laguerre filter

Lai

Poursaberi

Yanushkevich

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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Local vs. Global

Riccio

Casanova

Fenu

Face Recognition in Adverse Conditions

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Face recognition in real world applications is a very difficult task because of image misalignments, pose and illumination variations, or occlusions. Many researchers in this field have investigated both face representation and classification techniques able to deal with these drawbacks. However, none of them is free from limitations. Early proposed algorithms were generally holistic, in the sense they consider the face object as a whole. Recently, challenging benchmarks demonstrated that they are not adequate to be applied in unconstrained environments, despite of their good performances in more controlled conditions. Therefore, the researchers' attention is now turning on local features that have been demonstrated to be more robust to a large set of non-monotonic distortions. Nevertheless, though local operators partially overcome some drawbacks, they are still opening new questions (e.g., Which criteria should be used to select the most representative features?). This is the reason why, among all the others, hybrid approaches are showing a high potential in terms of recognition accuracy when applied in uncontrolled settings, as they integrate complementary information from both local and global features. This chapter explores local, global, and hybrid approaches.

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Local Gabor Fisher Classifier for Face Recognition

Cited by 3 publications

References 20 publications

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

One-shot facial feature extraction based on Gauss-Laguerre filter

Local vs. Global

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