If face images are degraded by block averaging, there is a nonlinear decline in recognition accuracy as block size increases, suggesting that identification requires a critical minimum range of object spatial frequencies. The identification of faces was measured with equivalent Fourier low-pass filtering and block averaging preserving the same information and with high-pass transformations. In Experiment 1, accuracy declined and response time increased in a significant nonlinear manner in all cases as the spatial-frequency range was reduced. However, it did so at a faster rate for the quantized and high-passed images. A second experiment controlled for the differences in the contrast of the high-pass faces and found a reduced but significant and nonlinear decline in performance as the spatial-frequency range was reduced. These data suggest that face identification is preferentially supported by a band of spatial frequencies of approximately 8-16 cycles per face; contrast or line-based explanations were found to be inadequate. The data are discussed in terms of current models of face identification.The questions of whether the information concerning the identity offaces is carried by a limited range ofspatial scales and whether the potential information from different regions ofthe spatial spectrum is given equal weight in the determination of identity have been approached in a number of different ways. One method ofconsidering these issues has been to make use ofspatial-frequencyfilteringtechniques (Harmon, 1973). However, variations in this method have produced contradictory results, with notably differentconclusions about the relative importance of different spatial-frequency bands specified in terms ofcycles per face. The term cycles perface is defmed as the number of sinusoidal repetitions of a given width that can be placed within the eye-level width of the face. The use ofthis metric to describe the information present in stimuli allows discussion ofthe degree of detail necessary for recognition, perhaps by defining the scale of facial configuration. A class ofobjects has a configuration if there is a consistent set of features all arranged in the same order. Thus, if a set ofexamples are superimposed, normalizing for scale and viewpoint, another example of the class is produced that is closer to the prototype. Clearly, faces have this property, since all have two eyes, a nose, and a mouth-and these are consistently arranged.Harmon ( can be seen in Figure 2. The images are formed by placing a regular square grid across the image and setting the pixel value at each grid square to the average gray level within it. This work suggested that the minimum image quality that allows effective identification corresponds to a 16 X 16 pixel image; however, since the images did not take up the whole of the screen, the number of pixels per face was slightly lower. Harmon also used a smooth low-pass filtering technique. This type of filtering operation does not introduce additional spatial frequencies (noise), as the pix...

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

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It has recently become apparent that if face images are degraded by spatial quantisation, or block averaging, there is a nonlinear acceleration of the decline in accuracy of recognition as block size increases. This suggests recognition requires a critical minimum range of object spatial frequencies. Two experiments were performed to clarify the phenomenon. In experiment 1, the speed and accuracy of recognition for six frontoparallel photographs of faces were measured. After familiarisation training sessions, the images were shown for 100 ms with 11, 21, and 42 pixels per face, horizontally measured. Transformations calculated to remove the same range of spatial frequencies were performed by means of quantisation, a Fourier low-pass filter, and Gaussian blurring. Although accuracy declined and speed increased in a significant, nonlinear manner in all cases as the image quality was reduced, it did so at a faster rate for the quantised images. In experiment 2, faces rated as being typical were shown at 9, 12, 23, and 45 pixels per face and with appropriate Fourier low-pass versions. The nonlinear decline was confirmed and it was shown that it could not be attributed to a ceiling effect. A further condition allowed quantised and Fourier low-pass conditions to be compared with an unstructured-noise condition of equal strength to that of the quantised images. These gave comparable, but slightly less impaired, recognition than the quantised images. It can be inferred from these results that the removal of a critical range of at least 8-16 cycles per face of information explains the step decline in recognition seen with quantised images. However, the decline found with quantised images is reinforced by internal masking from pixelisation.

A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as histograms and Gaussian mixture models have enabled the construction of suitably accurate skin detectors. However such techniques are not ideal for use in adaptive real time environments.We describe methods of skin detection using a Self-Organising Map or SOM, and show performance comparable (94% accuracy) to conventional techniques. We also introduce the AXEON Learning Processor as the basis for a hardware skin detector, and outline the potential benefits of using this system in a demanding environment, such as filtering Internet traffic, to which conventional techniques are not best suited.

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Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.