A survey on facial expression recognition in 3D video sequences

Danelakis, Antonios; Theoharis, Theoharis; Pratikakis, Ioannis

doi:10.1007/s11042-014-1869-6

Cited by 31 publications

(27 citation statements)

References 39 publications

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“…The position of the person with respect to the vision rig, as well as the relative distance between cameras and face, has not been controlled. This was chosen on purpose, since the aim of this work is to evaluate the performance of the system in operating conditions: modern identity verification systems, in fact, do not require constraining the person to a rigid frame and most of them work in completely unconstrained environments [3].…”

Section: The Identity Databasementioning

confidence: 99%

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Metrological characterization of 3D biometric face recognition systems in actual operating conditions

et al. 2017

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<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

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Section: The Identity Databasementioning

confidence: 99%

“…Moreover, FR systems exploit digital images and photogrammetry to provide contact-less identification [2], [3]. Those systems spread widely due to an increasing demand in the fields of security assurance and automatic identity verification [4] - [6].…”

Section: Introductionmentioning

confidence: 99%

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

et al. 2017

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“…However, we concentrate on the descriptors and the 4D representation used in expression recognition, which are also related to the retrieval process. A detailed survey on 4D video facial expression recognition methodologies is presented in [5]. In this survey, methodologies are reviewed and categorized based on the dynamic face analysis approach that they use.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to the recognition methodologies presented in [5], which apply dynamic face analysis belonging to the facial landmarks tracking-based sub-category, we should also refer to the work presented in [4]. This work is the only one dealing with 4D facial expression retrieval found in the bibliography.…”

Section: Related Workmentioning

confidence: 99%

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A robust spatio-temporal scheme for dynamic 3D facial expression retrieval

2015

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The problem of facial expression recognition in dynamic sequences of 3D face scans has received a significant amount of attention in the recent past whereas the problem of retrieval in this type of data has not. A novel retrieval scheme for such data is introduced in this paper. It is the first spatio-temporal retrieval scheme ever used for retrieval in dynamic sequences of 3D face scans. The proposed scheme automatically detects specific facial landmarks and uses them to create a spatio-temporal descriptor. At first, geometric as well as topological information of the 3D face scans is captured by using the detected landmarks. In the sequel, the aforementioned spatial information is filtered by using wavelet transformation, resulting to our final spatio-temporal descriptor. Our descriptor is invariant to the number of the 3D face scans of a facial expression sequence. The proposed retrieval scheme exploits the Square of Euclidean distance in order to compare descriptors corresponding to different 3D facial sequences. A detailed evaluation of the introduced retrieval scheme is presentedshowing that it outperforms previous state-of-the-art retrieval schemes. Experiments have been conducted using the six prototypical expressions of the standard data set BU − 4DFE. Finally, a majority voting methodology based on the retrieval results is used to achieve unsupervised dynamic 3D facial expression recognition. The achieved classification accuracy outperforms the state-of-the-art supervised dynamic 3D facial expression recognition techniques.

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