Face saliency in various human visual saliency models

Abstract

Under natural viewing conditions humans tend to fixate on specific parts of images that interest them naturally. A saliency map is the map of regions which are more prominent than other regions in terms of low level image properties such as intensity, color, and orientation. With some modifications it can be used to simulate the natural human fixations also known as gaze. There are numerous applications in the field of engineering, marketing and art that can benefit from understanding the human visual fixation such as image quality evaluation, label design etc. The objective of this research is to understand the factors that influence the saliency map and gaze map and to modify the saliency map in order to make it similar to the gaze map. Eye movements of 20 test subjects were captured using eye tracking equipment. Unsurprisingly it was observed that whenever faces were present in the images, the observers gazed at them. Three different algorithms Saliency by Itti et al., GAFFE by Rajashekara et al., GBVS by Harel et al. were modified by addition of face saliency module to generate modified maps corresponding to each of the algorithms. Experimental gaze maps were analyzed and compared with modified saliency map, modified GAFFE map and modified GBVS map.

Improving unsupervised saliency detection by migrating from RGB to multispectral images

“…Later on, many models appeared, improving different assets of this initial approach: the use of a log‐spectrum in the input image, using the information theory to extract salient information, using high‐level features, and supervised learning trained by large eye‐tracking datasets . Recently, the majority of leading benchmark models has been based on convolutional networks and deep learning techniques…”