Automatic human face detection from video sequences is an important component of intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database managemen...
详细信息
Automatic human face detection from video sequences is an important component of intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database management. This paper proposes an automatic and robust method to detect human faces from video sequences that combines feature extraction and face detection based on local normalization, Gabor wavelets transform and adaboost algorithm. The key step and the main contribution of this work is the incorporation of a normalization technique based on local histograms with optimal adaptive correlation (OAC) technique to alleviate a common problem in conventional face detection methods: inconsistent performance due to sensitivity to variation illuminations such as local shadowing, noise and occlusion. The approach uses a cascade of classifiers to adopt a coarse-to-fine strategy for achieving higher detection rates with lower false positives. The experimental results demonstrate a significant performance improvement gains and achieved by local normalization over methods without normalizations in real video sequences with a wide range of facial variations in color, position, scale, and varying lighting conditions. (C) 2008 Elsevier Ltd. All rights reserved.
Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filte...
详细信息
Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines.
Object detection by the traditional adaboost algorithm is very time-consuming mainly because the candidate feature number is large, and the feature number in the final strong detector is large. So this paper elevates ...
详细信息
Object detection by the traditional adaboost algorithm is very time-consuming mainly because the candidate feature number is large, and the feature number in the final strong detector is large. So this paper elevates 3 efficient optimization techniques and implements to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce the training time. Besides these, we use double thresholds to describe each feature, which can improve the efficient of each feature, and reduce the required feature number for the final strong classifier. The experiment result show that the training of our system is hundred time faster than previous systems.
The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important r...
详细信息
The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three adaboost classifiers. The base classifier algorithms in three adaboost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different adaboost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.
Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective....
详细信息
ISBN:
(纸本)9780769533049
Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm-Boost-SVM, which puts SVM into adaboost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples in the re-sampling process of adaboost, SVM appears to be more accurate. The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.
In order to improve the classification performance of classifiers, an approach of multiple classifiers ensemble based on feature selection (FSCE) is proposed in the paper. After attributes of the training data set are...
详细信息
ISBN:
(纸本)9780769533056
In order to improve the classification performance of classifiers, an approach of multiple classifiers ensemble based on feature selection (FSCE) is proposed in the paper. After attributes of the training data set are specially selected, the new data set is mapped into new training data sets. There is the number of attributes (the class attribute is ignored) of the new data sets. Then classifiers with better performance are selected from the classifiers that are trained in every small training data set. They are used to classify the corresponding small testing data sets that are disposed by attribute selection. FSCE is tested on the UCI benchmark data sets, and compared classification efficiency with member classifiers trained based on the algorithm of adaboost. In this way, the utility of FSCE can be proved in the paper.
This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based syst...
详细信息
This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the word level rather than making a single decision at the utterance level. The basic WDC algorithm also provides options for further modeling and decision strategy improvement. Two sets of classifiers are employed for WDC: a word classifier D-W(k) and an utterance classifier D-u. D-W(k) is boosted via the adaboost algorithm directly in the probability space instead of the traditional feature space. D. is boosted via the dialect dependency information of the words. For a small training corpus, it is difficult to obtain a robust statistical model for each word and each dialect. Therefore, a context adapted training (CAT) algorithm is formulated, which adapts the universal phoneme Gaussian mixture models (GMMs) to dialect-dependent word hidden Markov models (HMMs) via linear regression. Three separate dialect corpora are used in the evaluations that include the Wall Street Journal (American and British English), NATO N4 (British, Canadian, Dutch, and German accent English), and IME (eight British dialects). Significant improvement in dialect classification is achieved for all corpora tested.
Until now, existing pedestrian detection systems usually use global features (e.g. appearance or motion) of human body to detect pedestrian;however, the detection rate needs to be improved in many situations since som...
详细信息
ISBN:
(纸本)9781424408177
Until now, existing pedestrian detection systems usually use global features (e.g. appearance or motion) of human body to detect pedestrian;however, the detection rate needs to be improved in many situations since sometimes the global features can not be obtained. For example, a pedestrian may be partly covered by a car or his/her part may hide into the background. Therefore it is essential to adopt some local features of key parts of human body to assist pedestrian detection. In this paper, we propose a method using some key local features of human body to help pedestrian detection. Since the introduction of additional features will cost the system more time, in order to ensure the detection speed, we firstly use both appearance and motion global features of human body to select candidates, and then use local features of head and leg to do further confirmation. In the confirmation stage, we use three kinds of local features (head appearance, face color and hair color) to detect the head of each candidate;at the same time, we also choose some particular local appearance features to detect the leg. The experimental results indicate that this method can improve detection rate with almost the same detection speed;additionally, it can reduce false alarm sometimes.
The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based schemes. In this paper a hierarch...
详细信息
ISBN:
(纸本)9781424409723
The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based schemes. In this paper a hierarchical classifier for precise eye location algorithm is introduced. The new algorithm involves three main steps. First, a classifier based on adaboost algorithm is introduced to locate eye pair roughly. In order to get a reliable location, simple eye location is also introduced for checking the located results. Second, a precise eye location method based on Minimum Extreme Region (MER) is employed to detect eye candidates. Finally, a combine processing based on the above two results is used for the last location. The algorithm is tested on database CAS-PEAL, JAFFE and BiolD. The results prove the merit of this approach.
The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based *** this paper a hierarchical cla...
详细信息
The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based *** this paper a hierarchical classifier for precise eye location algorithm is *** new algorithm involves three main ***, a classifier based on adaboost algorithm is introduced to locate eye pair *** order to get a reliable location, simple eye location is also introduced for checking the located ***, a precise eye location method based on Minimum Extreme Region (MER) is employed to detect eye ***, a combine processing based on the above two results is used for the last *** algorithm is tested on database CAS-PEAL, JAFFE and *** results prove the merit of this approach.
暂无评论