Antonios Danelakis

A survey on facial expression recognition in 3D video sequences

Facial expression recognition constitutes an active research area due to its various applications. This survey addresses methodologies for 3D mesh video facial expression recognition. Recognition is, actually, a special case of intra-class retrieval. The approaches are analyzed and compared in detail. They are primarily categorized according to the 3D dynamic face analysis technique used. In addition, currently available datasets, used for 3D video facial expression analysis, are presented. Finally, future challenges that can be addressed in order for 3D video facial expression recognition field to be further improved, are extensively discussed.

Blind image deconvolution using a banded matrix method

In this paper we study the blind image deconvolution problem in the presence of noise and measurement errors. We use a stable banded matrix based approach in order to robustly compute the greatest common divisor of two univariate polynomials and we introduce the notion of approximate greatest common divisor to encapsulate the above approach, for blind image restoration. Our method is analyzed concerning its stability and complexity resulting to useful conclusions. It is proved that our approach has better complexity than the other known greatest common divisor based blind image deconvolution techniques. Examples illustrating our procedures are given.

A spatio-temporal wavelet-based descriptor for dynamic 3D facial expression retrieval and recognition

Human emotions are often expressed by facial expressions and are generated by facial muscle movements. In recent years, the analysis of facial expressions has emerged as an active research area due to its various applications such as human–computer interaction, human behavior understanding, biometrics, emotion recognition, computer graphics, driver fatigue detection, and psychology. A novel analysis of dynamic 3D facial expressions using the positional information of automatically detected facial landmarks and the wavelet transformation is presented, which results in the proposed spatio-temporal descriptor. This descriptor is employed within the current paper in a retrieval scheme for dynamic 3D facial expression datasets and is thoroughly evaluated. Experiments have been conducted using the six prototypical expressions of the publicly available BU-4DFE dataset as well as the eight expressions included in the newly released publicly available BP4D-Spontaneous dataset. The obtained retrieval results outperform the retrieval results of the state-of-the-art methodologies. Furthermore, the retrieval results are exploited to achieve unsupervised dynamic 3D facial expression recognition. The aforementioned unsupervised procedure achieves better recognition accuracy compared to supervised dynamic 3D facial expression recognition state-of-the-art techniques.

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

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 presented showing that it outperforms previous state-of-the-art retrieval schemes. Experiments have been conducted using the six prototypical expressions of the standard data set

An effective methodology for dynamic 3D facial expression retrieval

\textit{BU}-4\textit{DFE}

Action unit detection in 3D facial videos with application in facial expression retrieval and recognition

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.

A spatio-temporal descriptor for dynamic 3 D facial expression retrieval and recognition

Recently, a lot of research has been dedicated to address the problem of facial expression recognition in dynamic sequences of 3D face scans. On the contrary, no research has been conducted on facial expression retrieval using dynamic 3D face scans. This paper illustrates the first results on the area of dynamic 3D facial expression retrieval. To this end, a novel descriptor is created, namely GeoTopo, capturing the topological as well as the geometric information of the 3D face scans along time. Experiments have been implemented using the angry, happy and surprise expressions of the publicly available dataset BU -- 4DFE. The obtained retrieval results are very promising. Furthermore, a methodology which exploits the retrieval results, in order to achieve unsupervised dynamic 3D facial expression recognition, is presented. The aforementioned unsupervised methodology achieves classification accuracy comparable to the supervised dynamic 3D facial expression recognition state-of-the-art techniques.

A hybrid method for computing the intersection and tangency points of plane curves

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 methodology for such data is introduced in this paper. The proposed methodology automatically detects specific facial landmarks and uses them to create a descriptor. This descriptor is the concatenation of three sub-descriptors which capture topological as well as geometric information of the 3D face scans. The motivation behind the proposed hybrid facial expression descriptor is the fact that some facial expressions, like happiness and surprise, are characterized by obvious changes in the mouth topology while others, like anger, fear and sadness, produce geometric but no significant topological changes. The proposed retrieval scheme exploits the Dynamic Time Warping technique in order to compare descriptors corresponding to different 3D facial sequences. A detailed evaluation of the introduced retrieval scheme is presented showing that it outperforms previous state-of-the-art retrieval schemes. Experiments have been conducted using the six prototypical expressions of the standard dataset BU-4DFE and the eight prototypical expressions of the recently available dataset BP4D-Spontaneous. Finally, a majority voting scheme based on the retrieval results is used to achieve unsupervised dynamic 3D facial expression recognition. The achieved classification accuracy is comparable to the state-of-the-art supervised dynamic 3D facial expression recognition techniques. HighlightsWe illustrate a novel retrieval methodology for dynamic sequences of 3D face scans.We present a detailed evaluation of the introduced retrieval scheme.BU-4DFE and BP4D-Spontaneous data sets were used for experiments.Retrieval results are used to achieve unsupervised facial expression recognition.The presented results outperform state-of-the-art retrieval schemes.