Perceptual Annotation: Measuring Human Vision to Improve Computer Vision

For many problems in computer vision, human learners are considerably better than machines. Humans possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. We propose to use visual psychophysics to directly leverage the abilities of human subjects to build better machine learning systems. First, we use an advanced online psychometric testing platform to make new kinds of annotation data available for learning. Second, we develop a technique for harnessing these new kinds of information – “perceptual annotations” – for support vector machines. A key intuition for this approach is that while it may remain infeasible to dramatically increase the amount of data and high-quality labels available for the training of a given system, measuring the exemplar-by-exemplar difficulty and pattern of errors of human annotators can provide important information for regularizing the solution of the system at hand. A case study for the problem face detection demonstrates that this approach yields state-ofthe- art results on the challenging FDDB data set.Engineering and Applied SciencesMolecular and Cellular Biolog

Face classification using PCA and K-nearest neighbor method

Principle Component Analysis is one of the most useful method using for human face recognition, our aim in this research is to implement an understandable recognition program with language R; furthermore, comparing K-Nearest Neighbor classifier with two different distance measurement and Support Vector Machines on their efficiency. PCA indeed reduce the run time of processing massive face images data and reach an ideal accuracy in controlled circumstances. We found that Euclidean K-NN classifier generally has better accuracy than Manhattan K-NN classifier; however, it suggested that they are suitable to use for smaller k value, especially one. The eigenfaces applied to construct the face space is essential as well, we reached best accuracies when selecting around 10% to 20% of eigens. However, the reconstruction suggests in an opposite way, more eigenfaces help the rebuilt much ideally; more than half of eigenvectors selection can reconstruct the face to be easily recognised as the original people

Upper Facial Action Unit Recognition

This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (AUs) by using Facial Action Coding System (FACS). Haar wavelet, Haar-Like and Gabor wavelet coe cients are compared, using Adaboost for feature selection. The binary classi cation results by using Support Vector Machines (SVM) for the upper face AUs have been observed to be better than the current results in the literature, for example 96.5% for AU2 and 97.6% for AU5. In multi-class classi cation case, the Error Correcting Output Coding (ECOC) has been applied. Although for a large number of classes, the results are not as accurate as the binary case, ECOC has the advantage of solving all problems simultaneously; and for large numbers of training samples and small number of classes, error rates are improved