F.Negin, Pau Rodriguez, M.Koperski, A.Kerboua, Jordi Gonzalez, J.Bourgeois, et al. (2018). PRAXIS: Towards automatic cognitive assessment using gesture recognition. ESWA - Expert Systems with Applications, 106, 21–35.
Abstract: Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults.
In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames.
We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis.
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F.X. Perez, F. Javier Sanchez, Xavier Binefa, Xavier Roca, Jordi Vitria, & Juan J. Villanueva. (1993). A mathematical morphology-based system for IC´s inspection and analysis. In Institute of Physics Conferences Series (Vol. 135, 381–384). Institute of Physics.
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Fadi Dornaika, A.Assoum, & Bogdan Raducanu. (2012). Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 575–583). LNCS. Springer Berlin Heidelberg.
Abstract: A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis.
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Fadi Dornaika, Abdelmalik Moujahid, & Bogdan Raducanu. (2013). Facial expression recognition using tracked facial actions: Classifier performance analysis. EAAI - Engineering Applications of Artificial Intelligence, 26(1), 467–477.
Abstract: In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%.
Keywords: Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2012). LSDA Solution Schemes for Modelless 3D Head Pose Estimation. In IEEE Workshop on the Applications of Computer Vision (pp. 393–398).
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2013). Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition. In Human Behavior Understanding 4th International Workshop (Vol. 8212, pp. 124–135). Springer International Publishing.
Abstract: Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods.
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Fadi Dornaika, & Angel Sappa. (2005). SFM for Planar Scenes: a Direct and Robust Approach.
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Fadi Dornaika, & Angel Sappa. (2005). Appearance-based 3D Face Tracker: An Evaluation Study.
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Fadi Dornaika, & Angel Sappa. (2006). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data.
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Fadi Dornaika, & Angel Sappa. (2006). 3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm. In 21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541.
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Fadi Dornaika, & Angel Sappa. (2006). Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models. In 8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684.
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Fadi Dornaika, & Angel Sappa. (2006). 3D Motion from Image Derivatives using the Least Trimmed Square Regression. In International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84.
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Fadi Dornaika, & Angel Sappa. (2007). Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters. In Int. Conf. on Image Analysis and Recognition, (Vol. 4633, 469–480). LNCS.
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Fadi Dornaika, & Angel Sappa. (2007). SFM for Planar Scenes: a Direct and Robust Approach. In book chapter: Informatics in Control, Automation and Robotics II, Ed. J. Filipe, J. Ferrier, J. Cetto and M. Carvalho, pp. 129–136. (best papers ICINCO 2005).
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Fadi Dornaika, & Angel Sappa. (2007). Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models. PRL - Pattern Recognition Letters, 28(15), 2116–2126.
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