Download e-book for kindle: Boosting-Based Face Detection and Adaptation by Cha Zhang, Zhengyou Zhang, Sven Dickinson, Gerard Medioni

By Cha Zhang, Zhengyou Zhang, Sven Dickinson, Gerard Medioni

ISBN-10: 160845133X

ISBN-13: 9781608451333

Face detection, as a result of its mammoth array of functions, is likely one of the so much energetic study parts in machine imaginative and prescient. during this ebook, we assessment a variety of techniques to stand detection constructed some time past decade, with extra emphasis on boosting-based studying algorithms. We then current a chain of algorithms which are empowered by means of the statistical view of boosting and the concept that of a number of example studying. we begin through describing a boosting studying framework that's able to deal with billions of educating examples. It differs from conventional bootstrapping schemes in that no intermediate thresholds have to be set in the course of education, but the full variety of unfavourable examples used for characteristic choice continues to be consistent and targeted (on the terrible acting ones). A a number of example pruning scheme is then followed to set the intermediate thresholds after boosting studying. This set of rules generates detectors which are either quickly and actual. desk of Contents: a quick Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / a number of example studying for Face Detection / Detector version / different functions / Conclusions and destiny paintings

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Extra resources for Boosting-Based Face Detection and Adaptation

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All the training examples are initially stored on a disk drive. Denote the training example set as S = {(xi , zi ), i = 1, · · · , N }, where zi = 0 for negative examples and zi = 1 for positive examples. Let N+ be the number of positive examples and N− be the number of negative examples. N+ + N− = N, and N− is usually on the order of billions. Each training example are associated with an AdaBoost weight. 1. SOFT-CASCADE TRAINING 31 Weight/score update Large size training set (on disk) uniform sampling Weight trimming yes Weight/score update Medium size training set (in memory) Small size training set (in memory) Importance sampling no Update whole set?

2004b) proposed a cascade learning algorithm based on forward feature selection (Webb, 1999), which is two orders of magnitude faster than the traditional approaches. The idea is to first train a set of weak classifiers that satisfy the maximum false positive rate requirement of the entire detector. During feature selection, these weak classifiers are added one by one, each making the largest improvement to the ensemble performance. Weighting of the weak classifiers can be conducted after the feature selection step.

Update scores of all examples in the medium size training set. 5. If t belong to set A = {2, 4, 8, 16, 32, · · · }, • Update scores and weights of the whole training set using the previously selected weak classifiers f1 , · · · , ft . • Perform weight trimming to trim 10% of the negative weights. • Take all positive examples and randomly sample Q negative examples from the trimmed training set. • Update negative example weight ratio ηneg (Eq. 6)). 6. Set preliminary rejection threshold θ (t) of all positive examples at stage t.

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Boosting-Based Face Detection and Adaptation by Cha Zhang, Zhengyou Zhang, Sven Dickinson, Gerard Medioni


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