Real-time image recognition using HLAC features at 1000 fps

Ishii, Idaku; Sukenobe, Ryo; Yamamoto, Kenkichi; Takaki, Takeshi

doi:10.1109/robio.2009.5420721

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A Higher-order Local Autocorrelation1

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Cited by 6 publications

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“…x(aJ, az , ..... , aN ) = S f(r)f(r +al ) ... .f(r +aN )dr (2) f (r) denotes the intensity of the observed pixel r, and ah a2 ... aN are N displacements. HLAC features are primitive image features based on the equation (2).…”

Section: A Higher-order Local Autocorrelationmentioning

confidence: 99%

Proficient facial expression recognition using HLACLF extraction with a bank of Bayesian classifiers for recognition

Mary.

Krishnaveni

2012

2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET)

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This paper presents a proficient method for extracting the HLAC-like features. Different masks from 2D-monochrome image sequences will be used for extracting the features. The Mutual Information Quotient (MIQ) is the basis for selecting the most relevant features. In this study, a bank of Bayesian classifier is adopted for recognition. The distinguished features are given to a bank of seven parallel Bayesian classifiers. For recognlzmg a particular facial expression, each Bayesian classifier is trained. The outputs of all classifiers are then combined using a maximum function. The test will be performed on images from the JAFFE database.

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Section: A Higher-order Local Autocorrelationmentioning

confidence: 99%