Face Recognition Using Fractal Codes

In this paper we propose a new method for face recognition using fractal codes. Fractal codes represent local contractive, affine transformations which when iteratively applied to range-domain pairs in an arbitrary initial image result in a fixed point close to a given image. The transformation parameters such as brightness offset, contrast factor, orientation and the address of the corresponding domain for each range are used directly as features in our method. Features of an unknown face image are compared with those pre-computed for images in a database. There is no need to iterate, use fractal neighbor distances or fractal dimensions for comparison in the proposed method. This method is robust to scale change, frame size change and rotations as well as to some noise, facial expressions and blur distortion in the imag

Infra-red Pupil Detection for Use in a Face Recognition System

This paper presents a new method of eye localisation and face segmentation for use in a face recognition system. By using two near infrared light sources, we have shown that the face can be coarsely segmented, and the eyes can be accurately located, increasing the accuracy of the face localisation and improving the overall speed of the system. The system is able to locate both eyes within 25% of the eye-to-eye distance in over 96% of test cases

A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.

Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation of large-scale brain networks remain completely unknown. Here, we use functional magnetic resonance imaging (fMRI) to investigate the mechanisms underlying switching of brain networks in three different experiments. We first examined this switching process in an auditory event segmentation task. We observed significant activation of the CEN and deactivation of the DMN, along with activation of a third network comprising the right fronto-insular cortex (rFIC) and anterior cingulate cortex (ACC), when participants perceived salient auditory event boundaries. Using chronometric techniques and Granger causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention ''oddball'' task and (ii) a task-free resting state. These results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities. Our findings have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control. brain networks ͉ cognitive control ͉ insula ͉ attention ͉ prefrontal cortex O ne distinguishing feature of the human brain, compared with brains lower on the phylogenetic ladder, is the amount of cognitive control available for selecting, switching, and attending to salient events in the environment. Recent research suggests that the human brain is intrinsically organized into distinct functional networks that support these processes (1-4). Analysis of resting-state functional connectivity, using both model-based and model-free approaches, has suggested the existence of at least three canonical networks: (i) a centralexecutive network (CEN), whose key nodes include the dorsolateral prefrontal cortex (DLPFC), and posterior parietal cortex (PPC); (ii) the default-mode network (DMN), which includes the ventromedial prefrontal cortex (VMPFC) and posterior cingulate cortex (PCC); and (iii) a salience network (SN), which includes the ventrolateral prefrontal cortex (VLPFC) and anterior insula (jointly referred to as the fronto-insular cortex; FIC) and the anterior cingulate cortex (ACC) In a recent meta-analysis, Dosenbach and colleagues hypothesized that several brain regions that overlap with the CEN and SN are important for multiple cognitive control functions, including initiation, maintenance, and adjustment of attention (7). However, no studies to date have directly assessed the temporal dynamics and causal interactions of specific nodes within the CEN, SN, and DMN. Converging evidence from a number of brain imaging studies across several task domains suggests that the FIC and ACC nodes of the SN, in particular, respond to the degree of subjective salience, whether cognitive, homeostatic, or emotional We used three functional magnetic resonance imaging (fMRI) experiments to examine the interaction between the SN, CEN, and DMN, with particular interest in the role of the FIC/ACC in regulating these networks. In the first experiment, we scanned 18 participants as they listened with focused attention to classical music symphonies inside the scanner. We analyzed brain responses during the occurrence of ''movement transitions:'' salient, orienting events arising from transitions between adjacent ''movements'' in the music (19). To specifically elucidate the role of the FIC in driving network changes, we used chronometry and Granger Causality Analysis (GCA), to provide information about the dynamics and directionality of signaling in cortical circuits In the second experiment, we investigated the generality of network switching mechanisms involving the FIC by examining brain responses elicited during a visual "oddball" attention task (23). A third experiment examined whether the network switching mechanism could be observed during task-free resting state where there was no overt task and no behavioral response (4). Our motivation for examining the resting-state fMRI data was the recent finding, based on computer simulation of large-scale brain networks, that even in the absence of external stimuli, certain nodes can regulate other nodes and function as hubs (24). NEUROSCIENCE Our aim was to test the hypothesis that common network switching mechanisms apply across tasks with varying cognitive demands and differing stimulus modalities. If confirmed, our findings would provide insights into fundamental control mechanisms in the human brain. Results We describe findings from Experiment 1 in the first three sections. Convergent findings from Experiments 2 and 3 are described subsequently. Activation of CEN and SN, and Deactivation of DMN During Auditory Event Segmentation. As reported previously (19), we found robust right-lateralized activation in the DLPFC, PPC, and FIC during ''movement transitions'' in the auditory event segmentation task. Here, we extend these findings to characterize network-specific responses in the CEN, DMN, and SN. Activations in the CEN and SN were found to be accompanied by robust deactivation in the DMN at the movement transition [ Latency Analysis Reveals Early Activation of the rFIC Relative to the CEN and DMN. First, we identified differences in the latency of the event-related fMRI responses across the entire brain using the method developed by Henson and colleagues (26). Briefly, this method provides a way to estimate the peak latency of the BOLD response at each voxel using the ratio of the derivative to canonical parameter estimates (see SI Materials and Methods for details). This analysis revealed that the event-related fMRI signal in the right FIC (rFIC) and ACC peaks earlier compared to the signal in the nodes of the CEN and DMN, indicating that the neural responses in the rFIC and ACC precede the CEN and DMN (see GCA Reveals that the rFIC Is a Causal Outflow Hub at the Junction of the CEN and DMN. Finally, to elucidate the dynamic interactions between the three networks we applied GCA. Briefly, GCA detects causal interactions between brain regions by assessing the Activations height and extent thresholded at the P Ͻ 0.001 level (uncorrected). The ICA prunes out extraneous activation and deactivation clusters visible in the GLM analysis to reveal brain regions that constitute independent and tightly coupled networks. Fig. 3. Granger causality analysis (GCA) of the six key nodes of the Salience (blue nodes), Central-Executive (green nodes) and Default-Mode (yellow nodes) networks during (A) auditory event segmentation, (B) visual oddball attention task, and (C) task-free resting state. GCA revealed significant causal outflow from the rFIC across tasks and stimulus modalities. In each subfigure, the thickness of the connecting arrows between two regions corresponds to the strength of directed connection (F-value) normalized by the maximum F-value between any pair of regions for that task (''raw'' F-values reported in NEUROSCIENCE shorter path length than all of the other regions except the VMPFC (t test, P Ͻ 0.05); however, these differences did not remain significant after multiple comparison correction (data not shown). These results suggest that the rFIC is an outflow hub at the junction of the CEN and DMN. Converging Evidence from Two Additional fMRI Experiments. To provide converging evidence for the rFIC as a causal outflow hub, we analyzed fMRI data from two other experiments using the same GCA and network analyses methods described above: (i) a visual ''oddball'' attention experiment, and (ii) a task-free resting state experiment (see also SI Materials and Methods). We found a pattern of significant causal outflow from the rFIC that was strikingly similar to the auditory event segmentation experiment ( Discussion ICA revealed the existence of statistically independent CEN, DMN, and SN during task performance, extending our recent discovery of similar networks in task-free, resting-state, conditions (4). Our analysis indicates that the rFIC, a key node of the SN, plays a critical and causal role in switching between the CEN and the DMN (we use the term ''causal'' here, and in the following sections in the sense implied by, and consistent with, latency analysis, GCA and network analysis). The striking similarity of significant causal outflow from the rFIC across tasks, involving different stimulus modalities, indicates a general role for the rFIC in switching between two key brain networks. Furthermore, our replication of this effect in the task-free resting state suggests that the rFIC is a network hub that can also initiate spontaneous switching between the CEN and DMN (24). Our findings help to provide a more unified perspective on exogenous and endogenous mechanisms underlying cognitive control. In the SI Discussion, we suggest that these interactions are the result of neural, rather than vascular processes. Here, we focus on the neurobiological implications of our findings in the context of the three networks that we set out to examine; analyses of several other control regions (including the sensory and association cortices) that further clarify the crucial role of the FIC in the switching process are discussed in the SI Text. FIC-ACC Network Is Neuroanatomically Uniquely Positioned to Gen- erate Control Signals. In primates, anatomical studies have revealed that the insular cortex is reciprocally connected to multiple sensory, motor, limbic, and association areas of the brain (30, 31). The FIC and ACC themselves share significant topographic reciprocal connectivity and form an anatomically tightly coupled network ideally placed to integrate information from several brain regions (9, 10, 32). Indeed, analysis of the auditory and visual experiments in our study found coactivation of these regions during task performance, as in many other studies involving cognitively demanding tasks (7). Previous neurophysiological and brain imaging studies have shown that the FIC-ACC complex moderates arousal during cognitively demanding tasks and that the rFIC, in particular, plays a critical role in the interoceptive awareness of both stimulus-induced and stimulus-independent changes in homeostatic states (9, 10). Furthermore, the FIC and ACC share a unique feature at the neuronal level: The human FIC-ACC network has a specialized class of neurons with distinctive anatomical and functional features that might facilitate the network switching process that we report here. The von Economo neurons (VENs) are specialized neurons exclusively localized to the FIC and ACC (33). Based on the dendritic architecture of the VENs, Allman and colleagues have proposed that ''the function of the VENs may be to provide a rapid relay to other parts of the brain of a simple signal derived from information processed within FI and ACC.'' (34). We propose that the VENs may, therefore, constitute the neuronal basis of control signals generated by the FIC and ACC in our study. Taken together, these findings suggest that the FIC and ACC, anchored within the SN, are uniquely positioned to initiate control signals that activate the CEN and deactivate the DMN. Differential Roles of the rFIC, ACC, and Lateral Prefrontal Cortex in Initiating Control Signals. Many previous studies of attentional and cognitive control have reported coactivation of the FIC and Comparison of the net causal outflow (out-in degree) for the six key nodes of the Salience, Central-Executive, and Default-Mode networks as assessed by Granger causality analysis revealed that the rFIC has a significantly higher net causal outflow than the CEN and DMN regions across tasks (conventions as in 12572 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0800005105 Sridharan et al. ACC (7, Our findings help to synthesize these and other extant findings in the literature into a common network dynamical framework and they suggest a causal, and potentially critical, role for the rFIC in cognitive control. We propose that one fundamental mechanism underlying such control is a transient signal from the rFIC, which engages the brain's attentional, working memory and higher-order control processes while disengaging other systems that are not task-relevant. We predict that disruptions to these processes may constitute a key aspect of psychopathology in several neurological and psychiatric disorders, including frontotemporal dementia, autism, and anxiety disorders (34, 50, 51). More generally, our study illustrates the power of a unified network approach-wherein we first specify intrinsic brain networks and then analyze interactions among anatomically discrete regions within these networks during cognitive information processing-for understanding fundamental aspects of human brain function and dysfunction

Gaze-J2K: gaze-influenced image coding using eye trackers and JPEG 2000, Journal of Telecommunications and Information Technology, 2006, nr 1

The use of visual content in applications of the digital computer has increased dramatically with the advent of the Internet and world wide web. Image coding standards such as JPEG 2000 have been developed to provide scalable and progressive compression of imagery. Advances in image and video analysis are also making human-computer interaction multi-modal rather than through the use of a keyboard or mouse. An eye tracker is an example input device that can be used by an application that displays visual content to adapt to the viewer. Many features are required of the format to facilitate this adaptation, and some are already part of image coding standards such as JPEG 2000. This paper presents a system incorporating the use of eye tracking and JPEG 2000, called Gaze-J2K, to allow a customised encoding of an image by using a user’s gaze pattern. The gaze pattern is used to automatically determine and assign importance to fixated regions in an image, and subsequently constrain the encoding of the image to these regions

Development of Large-Scale Functional Brain Networks in Children

Large-scale rewiring of brain circuits in children leads to emergence of hierarchical organization in the mature adult brain

Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis

An integral part of any audio-visual speech processing (AVSP) system is the front-end visual system that detects facial-features (e.g., eyes and mouth) pertinent to the task of visual speech processing. The ability of this front-end system to not only locate, but also give a confidence measure that the facial-feature is present in the image, directly affects the ability of any subsequent post-processing task such as speech or speaker recognition. With these issues in mind, this paper presents a framework for a facial-feature detection system suitable for use in an AVSP system, but whose basic framework is useful for any application requiring frontal facial-feature detection. A novel approach for facial-feature detection is presented, based on an appearance paradigm. This approach, based on intraclass unsupervised clustering and discriminant analysis, displays improved detection performance over conventional techniques

The development of a new signal processing program at the Queensland University of Technology

The School of Electrical and Electronic Systems Engineering at Queensland University of Technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established Signal Processing Research Centre (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal processing at QUT, the laboratories developed to support it, and the criteria that influenced the design

An Adaptive Optical Flow Technique for Person Tracking Systems

Optical flow can be used to segment a moving object from its background provided the velocity of the object is distinguishable from that of the background, and has expected characteristics. Existing optical flow techniques often detect flow (and thus the object) in the background. To overcome this, we propose a new optical flow technique, which only determines optical flow in regions of motion. We also propose a method by which output from a tracking system can be fed back into the motion segmenter/optical flow system to reinforce the detected motion, or aid in predicting the optical flow. This technique has been developed for use in person tracking systems, and our testing shows that for this application it is more effective than other commonly used optical flow techniques. When tested within a tracking system, it works with an average position error of less than six and a half pixels, outperforming the current CAVIAR1 benchmark system