Partial matching of interpose 3D facial data for face recognition

Perakis, Panagiotis; Passalis, G.; Theoharis, T.; Toderici, George; Kakadiaris, Ioannis A.

doi:10.1109/btas.2009.5339019

Cited by 25 publications

(42 citation statements)

References 29 publications

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“…Thus, in almost any application that requires processing of 3D facial data, an initial registration, based on the landmark points' correspondence, is necessary in order to make a system fully automatic [1], [2]. The landmark detector must be pose invariant in order to allow the registration of both frontal and side facial scans [3], [4], [2], [5].…”

Section: Introductionmentioning

confidence: 99%

3D Facial Landmark Detection under Large Yaw and Expression Variations

Perakis

Passalis

Theoharis

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

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101

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Abstract-A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object's surface and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5 − 6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.

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Section: Introductionmentioning

confidence: 99%

3D Facial Landmark Detection under Large Yaw and Expression Variations

Perakis

Passalis

Theoharis

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

101

View full text Add to dashboard Cite

show abstract

“…In most of the existing works on 3D facial landmarking, 3D facial landmarks are estimated by computing the 3D shape-related feature, including shape index [14,15,34], effective energy [16], Gabor filter [17,18], local gradient [35] and curvature feature [36]. However, the accuracy on these prominent landmarks decreases drastically, including nose tip and the corner of eyes.…”

Section: Facial Landmarking On 3d Facial Datamentioning

confidence: 99%

“…Nair and Cavallaro [21] study 3D facial landmarking by building a statistical model to estimate landmarks coarsely, and then heuristics are applied to refine the locations. Perakis et al [14,15] study landmarking on 3D facial data under much more challenging conditions, such as the missing data caused by self occlusion. Zhao et al [20] proposed another method based on statistical models, who presented a model which take the both the relationship between each landmark and the local properties around each landmark into account.…”

Section: Facial Landmarking On 3d Facial Datamentioning

confidence: 99%

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A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

et al. 2018

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Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.

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“…There has been some work in localizing landmarks using only 3D data, but they do not tackle facial expressions directly. For instance, the landmark set detected by Perakis et al [71] lacks some key points for the purpose of FER and Nair and Cavallaro [72] do not consider the landmarks in the mouth region at all. In fact, most of the automatic approaches make use of the associated 2D texture image or video frames for landmark detection ( [36], [19], [20]).…”

Section: B Algorithm Improvementmentioning

confidence: 99%

3D facial expression recognition: A perspective on promises and challenges

Fang¹,

Zhao²,

Ocegueda³

et al. 2011

Face and Gesture 2011

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Abstract-This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial expression recognition methods are identified. We also call for standardized experimental protocols in order to draw fair and meaningful comparisons between different systems.

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Partial matching of interpose 3D facial data for face recognition

Cited by 25 publications

References 29 publications

3D Facial Landmark Detection under Large Yaw and Expression Variations

3D Facial Landmark Detection under Large Yaw and Expression Variations

A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

3D facial expression recognition: A perspective on promises and challenges

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