A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier. The time-varying ensemble parameters (confidence of weak classifiers) are regarded as sequential arriving states and their posterior distribution is estimated in a Bayesian manner. Therefore, both...

Recently machine learning based intrusion detection system developments have been subjected to extensive researches because they can detect both misuse detection and anomaly detection. In this paper, we propose an AdaBoost based algorithm for network intrusion detection system with single weak classifier. In this algorithm, the classifiers such as Bayes Net, Naïve Bayes and Decision tree are us...

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Infomation based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual informatio...

Recently, Viola and Jones [1] have proposed a detector using Adaboost to select and combine weak classifiers from a very large pool of weak classifiers, and it has been proven to be very successful for detecting faces. We have followed their approach and applied it to detect rear views of cars. The detector was carefully examined and was expanded in a number of ways, such as varying the type an...

The performance of face verification systems has steadily improved over the last few years. Stateof-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for...

AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present ...

This paper introduces a novel approach to facial expression recognition in video sequences. Low cost contour features are introduced to effectively describe the salient features of the face. Temporalboost is used to build classifiers which allow temporal information to be utilized for more robust recognition. Weak classifiers are formed by assembling edge fragments with chamfer scores. Detectio...

The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card ...

We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classifiers are used as weak learners. We start by providing conditions on the error of the weak learner which guarantee that the empirical error of the composite classifier is small. We then focus on conditions required in order to insure that the linear weak learner itself ach...

Machine learning methods are widely used in security tasks. However, the robustness of these models against motivated adversaries is unclear. In this work, we propose a generic method that simulates evasion attempts to evaluate the robustness of classifiers under attack. We report results from experiments automatically generating malware variants to evade classifiers, from which we have observe...