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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD |
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2022 |
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Journal of Ambient Intelligence and Humanized Computing |
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13 |
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591–611 |
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One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition. |
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HUPBA; no proj |
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Admin @ si @ RKE2022a |
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3660 |
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Author |
Ricardo Dario Perez Principi; Cristina Palmero; Julio C. S. Jacques Junior; Sergio Escalera |
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Title |
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals |
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2021 |
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IEEE Transactions on Affective Computing |
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TAC |
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12 |
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3 |
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607-621 |
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Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions. |
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1 July-Sept. 2021 |
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HuPBA; no proj |
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Admin @ si @ PPJ2019 |
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3312 |
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Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre |
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Continuous Generalized Procrustes Analysis |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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2 |
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659–671 |
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Procrustes analysis; 2D shape model; Continuous approach |
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PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the
standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.
To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).
CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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OR; HuPBA; 605.203; 600.046;MILAB |
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Admin @ si @ IPE2014 |
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2352 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Error-Correcting Output Codes Library |
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2010 |
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Journal of Machine Learning Research |
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JMLR |
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11 |
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661-664 |
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(Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier. |
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1532-4435 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010c |
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1286 |
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Author |
Oriol Pujol; Debora Gil; Petia Radeva |
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Title |
Fundamentals of Stop and Go active models |
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2005 |
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Image and Vision Computing |
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23 |
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8 |
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681-691 |
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Deformable models; Geodesic snakes; Region-based segmentation |
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An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. |
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Butterworth-Heinemann |
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Newton, MA, USA |
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0262-8856 |
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IAM;MILAB;HuPBA |
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IAM @ iam @ PGR2005 |
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1629 |
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