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Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Integrating Vision and Language for First Impression Personality Analysis |
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2018 |
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IEEE Multimedia |
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MULTIMEDIA |
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25 |
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2 |
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24 - 33 |
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The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness. |
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HUPBA; 602.133 |
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Admin @ si @ GAL2018 |
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3124 |
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Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Chenxu Zhao; Xu Zhang; Stan Z Li; Zhen Lei |
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Title |
Surveillance Face Anti-spoofing |
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Year |
2024 |
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IEEE Transactions on Information Forensics and Security |
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TIFS |
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19 |
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1535-1546 |
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Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL. |
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HUPBA |
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Admin @ si @ FLW2024 |
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3869 |
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Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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2018 |
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Informaciones Psiquiatricas |
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232 |
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47-59 |
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0210-7279 |
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HUPBA; no menciona |
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Admin @ si @ FAA2018 |
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3214 |
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Author |
Sergio Escalera |
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Title |
Multi-Modal Human Behaviour Analysis from Visual Data Sources |
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2013 |
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ERCIM News journal |
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ERCIM |
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95 |
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21-22 |
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The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction. |
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0926-4981 |
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HuPBA;MILAB |
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Admin @ si @ Esc2013 |
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2361 |
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Author |
Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza |
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Title |
Evolving weighting schemes for the Bag of Visual Words |
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2017 |
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Neural Computing and Applications |
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Neural Computing and Applications |
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28 |
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5 |
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925–939 |
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Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision |
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The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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Springer |
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HUPBA;MV; no menciona |
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Admin @ si @ EPE2017 |
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2743 |
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