Facial expression plays an important role in nonverbal social communication, emotion expression and affective recognition. To make the reorganization of facial expression more effectively, researchers try to recognize facial expression by the recognition of facial action units. In this paper, in order to identify lip AUs, we adopt Gabor wavelet transformation as the feature extraction method an...

AdaBoost !5] is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step ill AdaBoost is constructing a distribution over the training examples to crette each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by tLe previous base model in the sequence [6]. The idea is to m...

This paper presents a novel ensemble classifier generation technique RotBoost, which is constructed by combining Rotation Forest and AdaBoost. The experiments conducted with 36 real-world data sets available from the UCI repository, among which a classification tree is adopted as the base learning algorithm, demonstrate that RotBoost can generate ensemble classifiers with significantly lower pr...

We study two boosting algorithms, Coordinate Ascent Boosting and Approximate Coordinate Ascent Boosting, which are explicitly designed to produce maximum margins. To derive these algorithms, we introduce a smooth approximation of the margin that one can maximize in order to produce a maximum margin classifier. Our first algorithm is simply coordinate ascent on this function, involving a line se...

A multi-stage approach — which is fast, robust and easy to train — for a face-detection system is proposed. Motivated by the work of Viola and Jones [1], this approach uses a cascade of classifiers to yield a coarse-to-fine strategy to reduce significantly detection time while maintaining a high detection rate. However, it is distinguished from previous work by two features. First, a new stage ...

We construct a framework which allows an algorithm to turn the distributions produced by some boosting algorithms into polynomially smooth distributions (w.r.t. the PAC oracle’s distribution), with minimal performance loss. Further, we explore the case of Freund and Schapire’s AdaBoost algorithm, bounding its distributions to polynomially smooth. The main advantage of AdaBoost over other boosti...

Well known for its simplicity and effectiveness in classification, AdaBoost, however, suffers from overfitting when class-conditional distributions have significant overlap. Moreover, it is very sensitive to noise that appears in the labels. This paper tackles the above limitations simultaneously via optimizing a modified loss function (i.e., the conditional risk). The proposed approach has the...

As more large-scale camera systems are deployed around the world, the need for video privacy is becoming increasingly vital. State of the art face and people tracking systems are not yet sufficiently robust for this domain, due to changing lighting conditions, occlusions, and the need to be realtime. This has motivated us to instead track visual markers worn by individuals who wish to have thei...

The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, d...

This paper proposes the AdaBoost Gabor Fisher Classifier (AGFC) for robust face recognition, in which a chain AdaBoost learning method based on Bootstrap re-sampling is proposed and applied to face recognition with impressive recognition performance. Gabor features have been recognized as one of the most successful face representations, but it is too high dimensional for fast extraction and acc...