By Cha Zhang, Zhengyou Zhang, Sven Dickinson, Gerard Medioni
Face detection, due to its titanic array of functions, is without doubt one of the so much energetic examine components in desktop imaginative and prescient. during this ebook, we assessment numerous 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 way of the statistical view of boosting and the idea that of a number of example studying. we begin via describing a boosting studying framework that's able to deal with billions of teaching 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 unfavorable examples used for function choice continues to be consistent and centred (on the bad 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 quick and exact. 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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Additional info for Boosting-Based Face Detection and Adaptation
A BRIEF SURVEY OF THE FACE DETECTION LITERATURE pose labeling scheme is often unavailable. Seemann et al. , 2005) to explicitly handle and estimate viewpoints and articulations of an object category. The training examples were first clustered, with each cluster representing one articulation and viewpoint. Separate models were then trained for each cluster for classification. Shan et al. (2006) proposed an exemplar-based categorization scheme for multiview object detection. At each round of boosting learning, the algorithm selects not only a feature to construct a weak classifier, but also a set of exemplars to guide the learning to focus on different views of the object.
2002) used a pyramid structure to handle the task, as shown in Fig. 10(b). The detector pyramid consists of 3 levels. The first level of the pyramid works on faces at all poses; the second level detects faces between −90◦ and −30◦ (left profile), between −30◦ and 30◦ (frontal), and between 30◦ and 90◦ (right profile), respectively; the third level detects faces at 7 finer angles. Once a test window passes one level of the detector, it will be passed to all the children nodes for further decision.
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? 1: The flowchart of the proposed training procedure using importance sampling and weight trimming.