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Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans |
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Improved RGB-D-T based Face Recognition |
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Journal Article |
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Year |
2016 |
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IET Biometrics |
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BIO |
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5 |
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4 |
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297 - 303 |
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Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. |
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HuPBA;MILAB; |
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no |
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Admin @ si @ OCN2016 |
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2854 |
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Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
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Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
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Journal Article |
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Year |
2016 |
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Multimedia Tools and Applications |
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MTAP |
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75 |
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22 |
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14985-14990 |
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ISE; HUPBA |
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no |
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Admin @ si @ DDB2016 |
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2934 |
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Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
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Looking at People Special Issue |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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2-4 |
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141-143 |
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HUPBA; ISE; 600.119;MV;OR;MILAB |
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no |
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Admin @ si @ EGJ2018 |
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3093 |
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Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Audio-Visual Emotion Recognition in Video Clips |
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Journal Article |
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2019 |
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IEEE Transactions on Affective Computing |
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TAC |
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10 |
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1 |
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60-75 |
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This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. |
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1 Jan.-March 2019 |
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HUPBA; 602.143; 602.133;MILAB |
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Admin @ si @ NMN2017 |
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3011 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad |
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Title |
Locality Regularized Group Sparse Coding for Action Recognition |
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2017 |
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Computer Vision and Image Understanding |
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CVIU |
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158 |
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106-114 |
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Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition |
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Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. |
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HuPBA; no proj;MILAB |
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no |
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Admin @ si @ BGE2017 |
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3014 |
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