Guiding attention of faces through graph based visual saliency (GBVS)

  • Ravi Kant KumarEmail author
  • Jogendra Garain
  • Dakshina Ranjan Kisku
  • Goutam Sanyal
Research Article


In a general scenario, while attending a scene containing multiple faces or looking towards a group photograph, our attention does not go equal towards all the faces. It means, we are naturally biased towards some faces. This biasness happens due to availability of dominant perceptual features in those faces. In visual saliency terminology it can be called as ‘salient face’. Human’s focus their gaze towards a face which carries the ‘dominating look’ in the crowd. This happens due to comparative saliency of the faces. Saliency of a face is determined by its feature dissimilarity with the surrounding faces. In this context there is a big role of human psychology and its cognitive science too. Therefore, enormous researches have been carried out towards modeling the computer vision system like human’s vision. This paper proposed a graphical based bottom up approach to point up the salient face in the crowd or in an image having multiple faces. In this novel method, visual saliencies of faces have been calculated based on the intensity values, facial areas and their relative spatial distances. Experiment has been conducted on gray scale images. In order to verify this experiment, three level of validation has been done. In the first level, our results have been verified with the prepared ground truth. In the second level, intensity scores of proposed saliency maps have been cross verified with the saliency score. In the third level, saliency map is validated with some standard parameters. The results are found to be interesting and in some aspects saliency predictions are like human vision system. The evaluation made with the proposed approach shows moderately boost up results and hence, this idea can be useful in the future modeling of intelligent vision (robot vision) system.


Prominent face Relative visual saliency Spatial distance Intensity Visual attention 


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Ravi Kant Kumar
    • 1
    Email author
  • Jogendra Garain
    • 1
  • Dakshina Ranjan Kisku
    • 1
  • Goutam Sanyal
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology DurgapurDurgapurIndia

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