2021
DOI: 10.1049/bme2.12035
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Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition

Abstract: In this article, an effective approach is proposed to recognise the 2D+3D facial expression automatically based on orthogonal Tucker decomposition using factor priors (OTDFPFER). As a powerful technique, Tucker decomposition on the basis of the low rank approximation is often used to extract the useful information from the constructed 4D tensor composed of 3D face scans and 2D images aiming to maintain correlations and their structural information. Finding a set of projected factor matrices is our ultimate goa… Show more

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Cited by 1 publication
(5 citation statements)
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“…The performance comparisons with the state-of-the-art methods (i.e., [9,16,17,19,20,23]) are shown in Table 5 on Bosphorus database by using Setup IV. From this table, we can observe that our proposed approach obtains the highest recognition accuracy, while the method [19] gains the lowest one.…”
Section: Comparison With Other Methodsmentioning
confidence: 99%
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“…The performance comparisons with the state-of-the-art methods (i.e., [9,16,17,19,20,23]) are shown in Table 5 on Bosphorus database by using Setup IV. From this table, we can observe that our proposed approach obtains the highest recognition accuracy, while the method [19] gains the lowest one.…”
Section: Comparison With Other Methodsmentioning
confidence: 99%
“…Among the three protocols, two expressions of happiness and surprise are achieved better recognition results because of their higher facial deformation, whereas fear expression that can be confused with other five expressions is obtained worse results and is to a great degree confused with happiness expression. Meanwhile, it can be found that sadness expression in Setup I is achieved the best recognition result among the three protocols, and even indicates a certain improvement compared with those in [16,17,20,22,25,46].…”
Section: Performance Evaluation On Bu-3dfe Databasementioning
confidence: 97%
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