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Author Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari edit   pdf
url  openurl
  Title Two-stage Recognition and Beyond for Compound Facial Emotion Recognition Type Journal Article
  Year 2021 Publication (up) Electronics Abbreviated Journal ELEC  
  Volume 10 Issue 22 Pages 2847  
  Keywords compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning  
  Abstract Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ KAR2021 Serial 3642  
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  doi
openurl 
  Title Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine Type Journal Article
  Year 2018 Publication (up) Entropy Abbreviated Journal ENTROPY  
  Volume 20 Issue 11 Pages 809  
  Keywords hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image  
  Abstract In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ RKE2018 Serial 3198  
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Author Mohammad Naser Sabet; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari edit  url
doi  openurl
  Title Privacy-Constrained Biometric System for Non-cooperative Users Type Journal Article
  Year 2019 Publication (up) Entropy Abbreviated Journal ENTROPY  
  Volume 21 Issue 11 Pages 1033  
  Keywords biometric recognition; multimodal-based human identification; privacy; deep learning  
  Abstract With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.  
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ NBA2019 Serial 3313  
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Author Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari edit  url
doi  openurl
  Title Action recognition using single-pixel time-of-flight detection Type Journal Article
  Year 2019 Publication (up) Entropy Abbreviated Journal ENTROPY  
  Volume 21 Issue 4 Pages 414  
  Keywords single pixel single photon image acquisition; time-of-flight; action recognition  
  Abstract Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent
neural network.
 
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ OHC2019 Serial 3319  
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Author Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan edit  url
openurl 
  Title Statistical Machine Learning for Human Behaviour Analysis Type Journal Article
  Year 2020 Publication (up) Entropy Abbreviated Journal ENTROPY  
  Volume 25 Issue 5 Pages 530  
  Keywords action recognition; emotion recognition; privacy-aware  
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ MEA2020 Serial 3441  
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