Open AccessRobust Face Detection Based on Knowledge‐Directed Specification of Bottom‐Up Saliency
Author(s) -
Lee YuBu,
Lee Sukhan
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.1510.0123
Subject(s) - robustness (evolution) , computer science , face detection , artificial intelligence , computer vision , face (sociological concept) , pattern recognition (psychology) , process (computing) , facial recognition system , social science , biochemistry , chemistry , sociology , gene , operating system
This paper presents a novel approach to face detection by localizing faces as the goal‐specific saliencies in a scene, using the framework of selective visual attention of a human with a particular goal in mind. The proposed approach aims at achieving human‐like robustness as well as efficiency in face detection under large scene variations. The key is to establish how the specific knowledge relevant to the goal interacts with the bottom‐up process of external visual stimuli for saliency detection. We propose a direct incorporation of the goal‐related knowledge into the specification and/or modification of the internal process of a general bottom‐up saliency detection framework. More specifically, prior knowledge of the human face, such as its size, skin color, and shape, is directly set to the window size and color signature for computing the center of difference, as well as to modify the importance weight, as a means of transforming into a goal‐specific saliency detection. The experimental evaluation shows that the proposed method reaches a detection rate of 93.4% with a false positive rate of 7.1%, indicating the robustness against a wide variation of scale and rotation.