scholarly journals Early Stages of Figure–Ground Segregation during Perception of the Face–Vase

2011 ◽  
Vol 23 (4) ◽  
pp. 880-895 ◽  
Author(s):  
Michael A. Pitts ◽  
Antígona Martínez ◽  
James B. Brewer ◽  
Steven A. Hillyard
Keyword(s):  
Selective Attention ◽  
Temporal Sequence ◽  
Button Press ◽  
Positive Potential ◽  
Attention Task ◽  
Early Stages ◽  
Face Region ◽  
Neural Processes ◽  
The Face

The temporal sequence of neural processes supporting figure–ground perception was investigated by recording ERPs associated with subjects' perceptions of the face–vase figure. In Experiment 1, subjects continuously reported whether they perceived the face or the vase as the foreground figure by pressing one of two buttons. Each button press triggered a probe flash to the face region, the vase region, or the borders between the two. The N170/vertex positive potential (VPP) component of the ERP elicited by probes to the face region was larger when subjects perceived the faces as figure. Preceding the N170/VPP, two additional components were identified. First, when the borders were probed, ERPs differed in amplitude as early as 110 msec after probe onset depending on subjects' figure–ground perceptions. Second, when the face or vase regions were probed, ERPs were more positive (at ∼150–200 msec) when that region was perceived as figure versus background. These components likely reflect an early “border ownership” stage, and a subsequent “figure–ground segregation” stage of processing. To explore the influence of attention on these stages of processing, two additional experiments were conducted. In Experiment 2, subjects selectively attended to the face or vase region, and the same early ERP components were again produced. In Experiment 3, subjects performed an identical selective attention task, but on a display lacking distinctive figure–ground borders, and neither of the early components were produced. Results from these experiments suggest sequential stages of processing underlying figure–ground perception, each which are subject to modifications by selective attention.

It’s Not All About the Face: Variability Reveals Asymmetric Obligatory Processing of Faces and Bodies in Whole-Body Contexts

Perception ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 626-646 ◽  
Author(s):  
Catherine L. Reed ◽  
Cindy M. Bukach ◽  
Matthew Garber ◽  
Daniel N. McIntosh
Keyword(s):  
Selective Attention ◽  
Body Posture ◽  
Holistic Processing ◽  
Whole Body ◽  
Focused Attention ◽  
Attention Task ◽  
The Face ◽  
Open Question ◽  
Processing Mechanisms ◽  
Inverted Face

Researchers have sought to understand the specialized processing of faces and bodies in isolation, but recently they have considered how face and body information interact within the context of the whole body. Although studies suggest that face and body information can be integrated, it remains an open question whether this integration is obligatory and whether contributions of face and body information are symmetrical. In a selective attention task with whole-body stimuli, we focused attention on either the face or body and tested whether variation in the irrelevant part could be ignored. We manipulated orientation to determine the extent to which inversion disrupted obligatory face and body processing. Obligatory processing was evidenced as performance changes in discrimination that depended on stimulus orientation when the irrelevant region varied. For upright but not inverted face discrimination, participants could not ignore body posture variation, even when it was not diagnostic to the task. However, participants could ignore face variation for upright body posture discrimination but not for inverted posture discrimination. The extent to which face and body information necessarily influence each other in whole-body contexts appears to depend on both domain-general attentional and face- or body-specific holistic processing mechanisms.

Cognitive and affective benefits of colouring: Two randomized controlled crossover studies

2018 ◽  
Author(s):  
Nicola Jane Holt ◽  
Leah Furbert ◽  
Emily Sweetingham
Keyword(s):  
Selective Attention ◽  
Performance Measures ◽  
Divergent Thinking ◽  
Attention Task ◽  
Creative Cognition ◽  
Randomized Controlled ◽  
Mindful Attention ◽  
Crossover Studies ◽  
And Performance ◽  
Implicit Mood

The current research sought to replicate and extend work suggesting that coloring can reduce anxiety, asking whether coloring can improve cognitive performance. In two experiments undergraduates (N = 47; N = 52) colored and participated in a control condition. Subjective and performance measures of mood and mindfulness were included: an implicit mood test (Experiment 1) and a selective attention task (Experiment 2) along with a divergent thinking test. In both experiments coloring significantly reduced anxiety and increased mindfulness compared with control and baseline scores. Following coloring participants scored significantly lower on implicit fear, than the control condition, and significantly higher on selective attention and original ideation. Coloring may not only reduce anxiety, but also improve mindful attention and creative cognition.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

499 Trait anxiety and erp (nd) in a selective attention task

1998 ◽  
Vol 30 (1-2) ◽  
pp. 191-192
Author(s):  
S. Hayashida ◽  
S.-I. Niwa ◽  
K. Kobayashi ◽  
K. Itoh
Keyword(s):  
Selective Attention ◽  
Trait Anxiety ◽  
Attention Task

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

scholarly journals State model based face mask detection

2018 ◽  
Vol 7 (2.22) ◽  
pp. 35
Author(s):  
Kavitha M ◽  
Mohamed Mansoor Roomi S ◽  
K Priya ◽  
Bavithra Devi K
Keyword(s):  
Face Mask ◽  
Search Space ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
State Model ◽  
Face Region ◽  
Automatic Teller Machine ◽  
Foreground Region ◽  
The Face ◽  
Surveillance Mechanism

The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through  the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect  mask.  

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