Who Is in the Crowd? Deep Face Analysis for Crowd Understanding

Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Raimondo Schettini
Keyword(s):  
2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.


Author(s):  
Rafael Calvo ◽  
Sidney D'Mello ◽  
Jonathan Gratch ◽  
Arvid Kappas ◽  
Jeffrey F. Cohn ◽  
...  

2020 ◽  
Author(s):  
Michał Bola ◽  
Marta Paź ◽  
Łucja Doradzińska ◽  
Anna Nowicka

AbstractIt is well established that stimuli representing or associated with ourselves, like our own name or an image of our own face, benefit from preferential processing. However, two key questions concerning the self-prioritization mechanism remain to be addressed. First, does it operate in an automatic manner during the early processing, or rather in a more controlled fashion at later processing stages? Second, is it specific to the self-related stimuli, or can it be activated also by other stimuli that are familiar or salient? We conducted a dot-probe experiment to investigate the mechanism behind attentional prioritization of the selfface image and to tackle both questions. The former, by employing a backwards masking procedure to isolate the early and preconscious processing stages. The latter, by investigating whether a face that becomes visually familiar due to repeated presentations is able to capture attention in a similar manner as the self-face. Analysis of the N2pc ERP component revealed that the self-face image automatically captures attention, both when processed consciously and unconsciously. In contrast, the visually familiar face did not attract attention, neither in the conscious, nor in the unconscious condition. We conclude that the selfprioritization mechanism is early and automatic, and is not triggered by a mere visual familiarity. More generally, our results provide further evidence for efficient unconscious processing of faces, and for a dissociation between attention and consciousness.


2016 ◽  
Vol 76 (12) ◽  
pp. 13805-13834 ◽  
Author(s):  
Federica Marcolin ◽  
Enrico Vezzetti
Keyword(s):  

Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  

2020 ◽  
Vol 102 ◽  
pp. 103954 ◽  
Author(s):  
Markos Georgopoulos ◽  
Yannis Panagakis ◽  
Maja Pantic

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