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Building an effective representation for 3D face geometry is essential for face analysis tasks, that is, landmark detection, face recognition and reconstruction. This paper proposes to use a Multi‐Channel Facial Shape (MCFS) representation that consists of depth, hand‐engineered feature and attention maps to construct a 3D facial descriptor. And, a multi‐channel adjustment mechanism, named filtered squeeze and reversed excitation (FSRE), is proposed to re‐organize MCFS data. To assign a suitable weight for each channel, FSRE is able to learn the importance of each layer automatically in the training phase. MCFS and FSRE blocks collaborate together effectively to build a robust 3D facial shape representation, which has an excellent discriminative ability. Extensive experimental results, testing on both high‐resolution and low‐resolution face datasets, show that facial features extracted by our framework outperform existing methods. This representation is stable against occlusions, data corruptions, expressions and pose variations. Also, unlike traditional methods of 3D face feature extraction, which always take minutes to create 3D features, our system can run in real time. [ABSTRACT FROM AUTHOR]

Recent methods for 2D facial landmark localization perform well on close‐to‐frontal faces, but 2D landmarks are insufficient to represent 3D structure of a facial shape. For applications that require better accuracy, such as facial motion capture and 3D shape recovery, 3DA‐2D (2D Projections of 3D Facial Annotations) is preferred. Inferring the 3D structure from a single image is an ill‐posed problem whose accuracy and robustness are not always guaranteed. This paper aims to solve accurate 2D facial landmark localization and the transformation between 2D and 3DA‐2D landmarks. One way to increase the accuracy is to input more precisely annotated facial images. The traditional cascaded regressions cannot effectively handle large or noisy training data sets. In this paper, we propose a Mini‐Batch Cascaded Regressions (MBCR) method that can iteratively train a robust model from a large data set. Benefiting from the incremental learning strategy and a small learning rate, MBCR is robust to noise in training data. We also propose a new Cross‐Dimension Annotations Conversion (CDAC) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa. The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization. Moreover, CDAC can run efficiently at up to 110 fps on a 3.4 GHz‐CPU workstation. Thus, CDAC provides a solution to transform existing 2D alignment methods into 3DA‐2D ones without slowing down the speed. Training and testing code as well as the data set can be downloaded from https://github.com/SWJTU‐3DVision/CDAC. [ABSTRACT FROM AUTHOR]

Nowadays, facial mimicry studies have acquired a great importance in the clinical domain and 3D motion capture systems are becoming valid tools for analysing facial muscles movements, thanks to the remarkable developments achieved in the 1990s. However, the face analysis domain suffers from a lack of valid motion capture protocol, due to the complexity of the human face. Indeed, a framework for defining the optimal marker set layout does not exist yet and, up to date, researchers still use their traditional facial point sets with manually allocated markers. Therefore, the study proposes an automatic approach to compute a minimum optimized marker layout to be exploited in facial motion capture, able to simplify the marker allocation without decreasing the significance level. Specifically, the algorithm identifies the optimal facial marker layouts selecting the subsets of linear distances among markers that allow to automatically recognizing with the highest performances, through a k-nearest neighbours classification technique, the acted facial movements. The marker layouts are extracted from them. Various validation and testing phases have demonstrated the accuracy, robustness and usefulness of the custom approach. [ABSTRACT FROM AUTHOR]

Normally, modeling 3D face is an effective way for pose-invariant recognition, yet its expensive computation significantly discourages potential applications. In this paper, a simple and fully automatic panoramic image-based pose-invariant face recognition method is proposed to present excellent accuracy with low complexity. In this paper, a face shape model with local morphing treatment is first constructed and considered as the alignment standard to deal with all of the possible geometric distortion problems. During the recognition phase, a proposed systematically designed algorithm with morphing and the selection function are both utilized to significantly ease the negative effects of various poses within ±45° in yaw and ±22.5° in pitch. As demonstrated in experimental results, a similar accuracy as that of the 3D start-of-the-arts is achieved with much less computational complexity. [ABSTRACT FROM AUTHOR]

This study proposes a novel automatic method for facial landmark localization relying on geometrical properties of 3D facial surface working both on complete faces displaying different emotions and in presence of occlusions. In particular, 12 descriptors coming from Differential Geometry including the coefficients of the fundamental forms, Gaussian, mean, principal curvatures, shape index and curvedness are extracted as facial features and their local geometric properties are exploited to localize 13 soft-tissue landmarks from eye and nose areas. The method is deterministic and is backboned by a thresholding technique designed by studying the behaviour of each geometrical descriptor in correspondence to the locus of each landmark. Occlusions are managed by a detection algorithm based on geometrical properties which allows to proceed with the landmark localization avoiding the covered areas. Experimentations were carried out on 3132 faces of the Bosphorus database and of a 230-sized internal database, including expressive and occluded ones (mouth, eye, and eyeglasses occlusions), obtaining 4.75 mm mean localization error. [ABSTRACT FROM AUTHOR]

Humans present clear demographic traits which allow their peers to recognize their gender and ethnic groups as well as estimate their age. Abundant literature has investigated the problem of automated gender, ethnicity and age recognition from facial images. However, despite the co-existence of these traits, most of the studies have addressed them separately, very little attention has been given to their correlations. In this work, we address the problem of joint demographic estimation and investigate the correlation through the morphological differences in 3D facial shapes. To this end, a set of facial features are extracted to capture the 3D shape differences among the demographic groups. Then, a correlation-based feature selection is applied to highlight salient features and remove redundancy. These features are later fed to Random Forest for gender and ethnicity classification, and age estimation. Extensive experiments conducted on FRGCv2 dataset, under Expression-Dependent and Expression-Independent settings, demonstrate the effectiveness of the proposed approaches for the three traits, and also show the accuracy improvement when considering their correlations. To the best of our knowledge, this is the first study exploring the correlations of these facial soft-biometric traits using 3D faces. This is also the first work which studies the problem of age estimation from 3D Faces. 1 1 Part of this work has been published in the International Conference on Computer Vision Theory and Applications 2014 [61] and won the Best Paper Award in the area of Image and Video Understanding . [ABSTRACT FROM AUTHOR]

3D face was recently investigated for various applications, including biometrics and diagnosis. Describing facial surface, i.e. how it bends and which kinds of patches is composed by, is the aim of studies of Face Analysis, whose ultimate goal is to identify which features could be extracted from three-dimensional faces depending on the application. In this study, we propose 105 novel geometrical descriptors for Face Analysis. They are generated by composing primary geometrical descriptors such as mean, Gaussian, principal curvatures, shape index, curvedness, and the coefficients of the fundamental forms, and by applying standard functions such as sine, cosine, and logarithm to them. The new descriptors were mapped on 217 facial depth maps and analysed in terms of descriptiveness of facial shape and exploitability for localizing landmark points. Automatic landmark extraction stands as the final aim of this analysis. Results showed that some newly generated descriptors were sounder than the primary ones, meaning that their local behaviours in correspondence to a landmark position is thoroughly specific and can be registered with high similarity on every face of our dataset. [ABSTRACT FROM AUTHOR]

Although human face averageness and symmetry are valuable clues in social perception (such as attractiveness, masculinity/femininity, and healthy/ sick), in the literature of facial attribute recognition, little consideration has been given to them. In this work, we propose to study the morphological differences between male and female faces by analyzing the averageness and symmetry of their 3D shapes. In particular, we address the following questions: (i) is there any relationship between gender and face averageness/symmetry? and (ii) if this relationship exists, which specific areas on the face are involved? To this end, we propose first to capture densely both the face shape averageness (AVE) and symmetry (SYM) using our Dense Scalar Field (DSF), which denotes the shooting directions of geodesics between facial shapes. Then, we explore such representations by using classical machine learning techniques, the Feature Selection (FS) methods and Random Forest (RF) classification algorithm. Experiments conducted on the FRGCv2 dataset show that a significant relationship exists between gender and facial averageness/symmetry when achieving a classification rate of 93.7% on the 466 earliest scans of subjects (mainly neutral) and 92.4% on the whole FRGCv2 dataset (including facial expressions). [ABSTRACT FROM AUTHOR]

In the last decades, several three-dimensional face recognition algorithms have been thought, designed, and assessed. What they have in common can be hardly said, as they differ in theoretical background, tools, and method. Here we propose a new 3D face recognition algorithm, entirely developed in Matlab ® , whose framework totally comes from differential geometry. First, 17 soft-tissue landmarks are automatically extracted relying on geometrical properties of facial shape. We made use of derivatives, coefficients of the fundamental forms, principal, mean, and Gaussian curvatures, and shape and curvedness indexes. Then, a set of geodesic and Euclidean distances, together with nose volume and ratios between geodesic and Euclidean distances, has been computed and summed in a final score, used to compare faces. The highest contribution of this work, we believe, is that its theoretical substratum is differential geometry with its various descriptors, which is something totally new in the field. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we develop a central profile-based 3D face pose estimation algorithm. The central profile is a unique curve on a 3D face surface that starts from forehead center, goes down through nose ridge, nose tip, mouth center, and ends at a chin tip. The points on the central profile are co-planar and belong to a symmetry plane that separates human face into two identical parts. The central profile is protrusive and has a certain length. Most importantly, the normal vectors of the central profile points are parallel to the symmetry plane. Based on the properties of the central profile, Hough transform is employed to determine the symmetry plane by invoking a voting procedure. An objective function is introduced in the parameter space to quantify the vote importance for face profile points and map the central profile to an accumulator cell with the maximal value. Subsequently, a nose model matching algorithm is used to detect nose tip on the central profile. A pitch angle estimation algorithm is also proposed. The pose estimation experiments completed for a synthetic 3D face model and the FRGC v2.0 3D database demonstrate the effectiveness of the proposed pose estimation algorithm. The obtained central profile detection rate is 99.9%, and the nose tip detection rate has reached 98.16% with error not larger than 10mm. [Copyright &y& Elsevier]

Here, we propose a new 3D representation designed in the objective of face recognition independently of the expressions. It is defined on localities presenting limited deformations following expression variations. The proposed representation is based on the multipolar parameterization that we recently introduced in Jribi et al. (2019) which is relative invariant under three dimensional Euclidean transformations and robust to the original mesh. A choice of the number of reference points of each multipolar parameterization is made according to the shape of the two types of region of the face namely those of nose and the eyes. The main curvature field is estimated on the parameterizations of each region. The parameters of dimensional reduction algorithms applied to the overall description are adjusted so that the recognition rates remain efficient. The experiments are carried out on three challenging 3D face databases: the FRGC v2.0, the BU-3DFE and Bosphorus. Very high rates are obtained for both identification and verification scenarios. These results are very competitive with state of the art methods. • Proposition of a novel SE(3) invariant face representation. • Independence with regard to the original mesh due to the multipolar parameterization. • Implementation of many local region multipolar parameterizations according to region shapes. • A study for the dimensional reduction of the proposed face representation and its impact on the recognition task. [ABSTRACT FROM AUTHOR]