Psychology, 2010, 1, 199-208 doi:10.4236/psych.2010.13027 Published Online August 2010 (http://www.SciRP.org/journal/psych) Copyright © 2010 SciRes. PSYCH 199 Tolerance of the ERP Signatures of Unfamiliar versus Familiar Face Perception to Spatial Quantization of Facial Images * Liisa Hanso 1 , Talis Bachmann 1,2 , Carolina Murd 1,2 1 University of Tartu, Institute of Psychology, Tartu, Estonia; 2 University of Tartu, Institute of Public Law, Tartu, Estonia. Email: talis.bachmann@ut.ee Received June 4 th , 2010; revised July 9 th , 2010; accepted July 12 th , 2010. ABSTRACT Processing of faces as stimuli is known to be associated with a conspicuous ERP component N170. Processing of fa- miliar faces is found to be associated with an increased amplitude of the ERP components N250r and P300, including when a subject wishes to conceal face familiarity. Leaving facial images without high spatial frequency content by low pass spatial filtering does not eliminate face-perception signatures of ERP. Here, for the first time, we tested whether these facial-processing ERP-signatures can be recorded also when facial images are spatially quantized by pixelation, a procedure where in addition to impoverishment of face-specific information by spatial-frequency filtering a compet- ing masking structure is generated by the square-shaped pixels. We found dependence of N170 expression on level of pixelation and P300 amplitudes dependent on familiarity with 21 pixels-per-face and 11 pixels-per-face images, but not with 6 pixels-per-face images. ERP signatures of facial information processing tolerate image degradation by spatial quantization down to about 11 pixels per face and this holds despite the subject’s wish to conceal his or her familiarity with some of the faces. Keywords: Face Recognition, Spatial Quantization, N170, P300, Deception 1. Introduction The ability to identify and discriminate faces is a major research field in cognitive neuroscience, cognitive psy- chology, artificial pattern recognition and forensic re- search [1-12]. Advancement of knowledge in this area promises considerable developments and gains in tech- nology, economy, security-state of society, etc. Among several urgent tasks, finding objective and reliable brain- process signatures of face recognition and familiar versus unfamiliar face discrimination can be especially empha- sised. Inter alia, electroencephalographic (EEG) event related potentials (ERPs) based methods have shown their good applicability for the above-mentioned pur- poses. EEG/ERP are a relatively cheap, non-invasive, well standardised and internationally quite widespread means to study brain-process signatures of processing meaningful object information, supported by an impres- sive amount of documented psychophysiological facts and regularities from basic and applied research. In practical applications of face recognition research, many directions have emerged and many important re- sults obtained. However, quite many unsolved or unex- plored problems remain [2,11]. For example, it may be the case that the images of facial stimuli that are to be shown to perceiving subjects (e.g., in order to evaluate if the subject recognises a face or identifies a familiar face) are degraded due to some technical problems or imper- fections. Often the available facial information is repre- sented as a pixelized image with poor resolution. It is useful to know whether these stimuli can be nevertheless used as critical stimuli for testing and expertise and what is the scale of degradation tolerated by the automatic face-processing routines in the brain so that meaningful and actually sensitive ERP signatures of face recognition and/or face familiarity can be still registered and evalu- ated. Up to now there is no face-identification ERP-re- search using poor-quality pixelated images. The three ERP components registered from the human scalp that are strongly involved in face processing are N170, N250r, and P300 [13-18]. N170 is a quite robust * This study was supported by Estonian Ministry of Education and Research through Scientific Competency Council (targeted financing research theme SF0182717s06, “Mechanisms of Visual Attention”).