Deciding the price of fame

Previous studies of ignorance-driven decision-making have either analyzed when ignorance should prove advantageous on theoretical grounds, or else they have examined whether human behavior is consistent with an ignorance driven inference strategy (e.g., the recognition heuristic). The current study merges these research goals by examining whether – under conditions where ignorance driven inference might be expected – the type of advantages theoretical analyses predict are evident in human performance data. A single experiment shows that, when asked to make relative wealth judgments, participants reliably use recognition as a basis for their judgments. Their wealth judgments under these conditions are reliably more accurate when some of the target names are unknown than when participants recognize all the names (the “less-is-more effect”). these data are robust against a number of variations on the size of the pool from which participants have to choose and the nature of the wealth judgment.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.