3D Landmarking in Multiexpression Face Analysis: A Preliminary Study on Eyebrows and Mouth

Vezzetti, Enrico; Marcolin, Federica

doi:10.1007/s00266-014-0334-2

Cited by 35 publications

(20 citation statements)

References 24 publications

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“…The derived geometric feature could be simple, with geometric features such as lip width, and with lengths describing the expression states such as how open the mouth is. Some research derived more complex geometric features, for example, calculating a set of geodesic and Euclidean distances, together with nose volume and ratios between geodesic and Euclidean distances; specifically, on the mouth and eyebrows as a separate study for facial expression analysis in paper [21].…”

Section: Features For Facial Expression Recognitionmentioning

confidence: 99%

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

et al. 2017

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Abstract:Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and corresponding high-performance classifier selection are still a big challenge. Specific features/classifiers display different performance in different datasets. There has currently been no consensus in the literature that any expression feature or classifier is always good in all cases. Although the recently updated deep learning algorithm, which uses learning deep feature instead of manual construction, appears in the expression recognition research, the limitation of training samples is still an obstacle of practical application. In this paper, we aim to find an effective solution based on a fusion and association learning strategy with typical manual features and classifiers. Taking these typical features and classifiers in facial expression area as a basis, we fully analyse their fusion performance. Meanwhile, to emphasize the major attributions of affective computing, we select facial expression relative Action Units (AUs) as basic components. In addition, we employ association rules to mine the relationships between AUs and facial expressions. Based on a comprehensive analysis from different perspectives, we propose a novel facial expression recognition approach that uses multiple features and multiple classifiers embedded into a stacking framework based on AUs. Extensive experiments on two public datasets show that our proposed multi-layer fusion system based on optimal AUs weighting has gained dramatic improvements on facial expression recognition in comparison to an individual feature/classifier and some state-of-the-art methods, including the recent deep learning based expression recognition one.

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Section: Features For Facial Expression Recognitionmentioning

confidence: 99%

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

et al. 2017

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“…Despite recent contributions have shown that automatic landmarking, even if soft-tissue-based, is possible [17][18][19][20][21][22][23][24][25][26][27][28], for many applications landmarks are manually localized. In particular, in the forensic context, landmarks are completely manually allocated.…”

Section: Landmark Allocationmentioning

confidence: 99%

Facial Landmarks for Forensic Skull-Based 3D Face Reconstruction: A Literature Review

Vezzetti

Marcolin

Tornincasa

et al. 2016

Lecture Notes in Computer Science

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Abstract. Recent Face Analysis advances have focused the attention on studying and formalizing 3D facial shape. Landmarks, i.e. typical points of the face, are perfectly suited to the purpose, as their position on visage shape allows to build up a map of each human being's appearance. This turns to be extremely useful for a large variety of fields and related applications. In particular, the forensic context is taken into consideration in this study. This work is intended as a survey of current research advances in forensic science involving 3D facial landmarks. In particular, by selecting recent scientific contributions in this field, a literature review is proposed for in-depth analyzing which landmarks are adopted, and how, in this discipline. The main outcome concerns the identification of a leading research branch, which is landmark-based facial reconstruction from skull. The choice of selecting 3D contributions is driven by the idea that the most innovative Face Analysis research trends work on three-dimensional data, such as depth maps and meshes, with three-dimensional software and tools. The third dimension improves the accurateness and is robust to colour and lightning variations.

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“…The upper lip outline is also evaluated, in order to provide shape and size description of the defect. The geometrical descriptors used in this work are defined and described in previous works of our research group on 3D faces at different ages Vezzetti et al, 2011;2014). In particular, geometrical descriptors are introduced and mapped point-by-point on adults' 3D facial surfaces to investigate their behaviours and study their local properties Vezzetti and Marcolin, 2012a;2012b).…”

Section: Fig 1 Multiplanar Image (Above) and Midsagittal Plane (Belmentioning

confidence: 99%

“…Then, these descriptors have been applied to automatic landmarking, i.e. extraction of typical facial points, in the medical context (Vezzetti and Marcolin, 2012c;Vezzetti et al, 2012;Vezzetti and Marcolin, 2014), for recognition (Vezzetti et al, 2014a) and foetal diagnosis (Vezzetti et al, 2014b) purposes.…”

Section: Fig 1 Multiplanar Image (Above) and Midsagittal Plane (Belmentioning

confidence: 99%

Diagnosing Cleft Lip Pathology in 3d Ultrasound: A Landmarking-Based Approach

et al. 2015

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The aim of this work is to automatically diagnose and formalize prenatal cleft lip with representative key points and identify the type of defect (unilateral, bilateral, right, or left) in three-dimensional ultrasonography (3D US). Geometry has been used as a framework for describing facial shapes and curvatures. Then, descriptors coming from this field are employed for identifying the typical key points of the defect and its dimensions. The descriptive accuracy of these descriptors has allowed us to automatically extract reference points, quantitative distances, labial profiles, and to provide information about facial asymmetry. Eighteen foetal faces, ten of healthy foetuses and eight with different types of cleft lips, have been obtained through a Voluson system and used for testing the algorithm. Cleft lip has been diagnosed and correctly characterized in all cases. Transverse and cranio-caudal length of the cleft have been computed and upper lip profile has been automatically extract to have a visual quantification of the overall labial defect. The asymmetry information obtained is consistent with the defect. This algorithm has been designed to support practitioners in identifying and classifying cleft lips. The gained results have shown that geometry might be a proper tool for describing faces and for diagnosis.

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3D Landmarking in Multiexpression Face Analysis: A Preliminary Study on Eyebrows and Mouth

Cited by 35 publications

References 24 publications

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

Facial Landmarks for Forensic Skull-Based 3D Face Reconstruction: A Literature Review

Diagnosing Cleft Lip Pathology in 3d Ultrasound: A Landmarking-Based Approach

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