Implementation of perceptual aspects in a face recognition algorithm

Crenna, Francesco; Zappa, Emanuele; Bovio, Luca; Testa, Rafael Luiz; Gasparetto, M.; Rossi, Giovanni Battista

doi:10.1088/1742-6596/459/1/012031

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…However, the quantities measured for the classification are affected by uncertainty, thus generating a risk in accepting the decision. This risk could be quantified and reduced by suitably taking into account the measurement uncertainty in the comparison stage [20][21][22], or using fuzzy logic [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Face recognition based on 3D features: Management of the measurement uncertainty for improving the classification

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Face recognition based on 3D features: Management of the measurement uncertainty for improving the classification

et al. 2015

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“…Some techniques are proposed to reduce this risk. They take into account the measurement uncertainty in the comparison stage [3]- [5], or adopt fuzzy logic [6], [7], belief function theory [8], Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

A proposal for improving the performance of face recognition systems based on 3d features

Betta

Capriglione

Corvino

et al. 2015

2015 XVIII AISEM Annual Conference

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In this paper a suitable methodology for the improvement of the reliability of results in classification systems based on 3D images is proposed. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image (obtained processing a pair of two 2D stereoscopic images) and on a suitable statistical approach providing a confidence level to the classification result. These pieces of information are then managed in order to improve the classification performance in terms of correct classification and missed classification percentages. The experimental results, obtained applying the methodology on an Active Appearance Models algorithm, a popular method for face recognition based on 3D features, show that, compared with a traditional approach (which generally does not take into account the uncertainty on 3D features), the proposed methodology allows to significantly improve the classification performance even in scenarios characterized by a high uncertainty.

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“…In [2][3][4][5] the most relevant techniques developed during last years have been reported, demonstrating good results and a certain degree of maturity when operating under constrained conditions. However, they still suffer high uncertainty if used in everyday situations of the practical life; due to this reason some authors analyzed the human perception [6] with the purpose of identifying human cognitive processes that operate during personal recognition, with the idea of implementing them in novel algorithms [7]. Most of the state-of-the-art recognition techniques are based on the analysis of some biometric measurements, provided by processing facial images with features extraction algorithms [8,9].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of 3D facial features measurements and its effects on personal identification

et al. 2014

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Implementation of perceptual aspects in a face recognition algorithm

Cited by 4 publications

References 7 publications

Face recognition based on 3D features: Management of the measurement uncertainty for improving the classification

Face recognition based on 3D features: Management of the measurement uncertainty for improving the classification

A proposal for improving the performance of face recognition systems based on 3d features

Uncertainty of 3D facial features measurements and its effects on personal identification

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