Studying Human Face Recognition with the Gaze-Contingent Window Technique

Studying Human Face Recognition with the Gaze-Contingent Window Technique Naing Naing Maw (nnmaw@cs.umb.edu) University of Massachusetts at Boston, Department of Computer Science 100 Morrissey Boulevard, Boston, MA 02125-3393, USA Marc Pomplun (marc@cs.umb.edu) University of Massachusetts at Boston, Department of Computer Science 100 Morrissey Boulevard, Boston, MA 02125-3393, USA achieve face recognition rates that are far below the ones of a human observer. We believe that a better understanding of the mechanisms underlying human face recognition will be beneficial to both the fields of medicine and machine vision. In the present study, we applied the sophisticated method of gaze- contingent windows to a psychophysical eye-movement study of a face recognition task in order to broaden our understanding of the underlying perceptual and attentional processes. The gaze-contingent window technique provides powerful experimental control and has been used extensively in reading, scene perception, and more recently in visual search studies (e.g. Bertera & Rayner, 2000; McConkie & Rayner, 1975; Pomplun, Reingold & Shen, 2001; Saida & Ikeda, 1979; see Rayner, 1998, for a review). In most of its applications, this technique obscures all objects from view except those within a certain window that is continually centered on the participant’s current gaze position. The window position changes across fixations to follow the gaze position. For example, in a study by McConkie and Rayner (1975), participants read text that was masked outside a visual window that included the fixated character and a number of characters to the left and to the right. Only the text within the window was legible. The visual span in reading was assessed by varying the window size across trials and determining the smallest window size that allowed participants to read with normal speed. In the present study, participants were presented with images of famous and non-famous faces and had to indicate whether they recognized the displayed person or not. While viewing the images, a gaze-contingent window was administered with its size varying across trials. This allowed us to address the following questions: First, to what extent does face recognition rely on the simultaneous availability of the entire face features? Second, from which positions in the image and in what manner do participants acquire information about a face when their peripheral vision is restricted? It is well known that saccades during unrestricted face viewing tend to be aimed at the region formed by the eyes, nose, and mouth (e.g., Yarbus, 1967). However, where and how do participants gather information if they have to do it sequentially and be as efficient as possible? Third, how can the moment of recognition be characterized? Is it possible to determine this moment based on psychophysical data? Abstract In eye-movement experiments using gaze-contingent windows, the stimulus display is continuously updated in response to the participant’s current gaze position. Usually, a window is centered at the participant’s gaze position and follows it wherever the participant looks. Within the window, all stimulus information is visible, while outside of the window at least part of the information is masked. In the present paper, we apply this technique to a face recognition task. By varying the size of the window, we gain insight into face recognition processes in humans and characterize the visual information on which face recognition relies. The results also motivate the use of gaze-contingent windows to study visual perception. Introduction Face recognition is a very important function of the human visual system and is fundamental to our complex social behavior. Therefore, it is not surprising that face recognition in humans has been extensively studied. Many studies concluded that face recognition relies more strongly on holistic information than does object recognition in general (see Maurer, Le Grand & Mondloch, 2002, for a review). In other words, ideally, the recognition process uses the entire visual information available from a face. What makes faces so special in this regard? An important reason seems to be our everyday-life expertise in identifying people by their faces. It was found that people can be trained to recognize individual non-face objects, and thereby develop analysis patterns that are similar to those used in face recognition (e.g., Diamond & Carey, 1986; Gauthier, Williams, Tarr & Tanaka, 1998). Other researchers presented participants with face images filtered by different spatial frequencies and found that only a rather narrow band of spatial frequencies (about 6 to 12 cycles per face width) contributes significantly to the recognition of a face (e.g., Nasanen, 1999). Again, this finding does not apply to non-face objects, even if individual objects of the same class are to be distinguished (Biederman & Kolacsai, 1997). Moreover, face recognition has received considerable attention in machine vision research (e.g., Phillips, Moon, Rizvi & Rauss, 2000; Senior, Hsu, Mottaleb & Jain, 2002; Zhou., Krueger & Chellappa, 2003). Despite these immense efforts, however, even the currently best vision algorithms