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Real-time object detection has many computer vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce greedy sparse linear...
In this paper, we describe how to classify frontal face from the results of face detection which include non-frontal faces. To do this, we use AdaBoost learning method with Modified Census Transform (MCT) to construct a two-class classifier. As a result of that, our frontal face classifier achieves high classification rate above 96% and fast performance about 10 frames/sec in mobile device.
Face detection is a challenging problem in the field of computer vision and a key component of machine intelligence. Viola and Jones put forward a face detection framework in 2001 that can process images extremely rapidly while achieving high detection rates. This paper extends the framework to handle rotated faces through a simple improvement. The method is to rotate the detected sub-windows by 30...
AdaBoost based training method has become a state-of-the-art boosting approach in face detection system. In this paper, compared to the naive AdaBoost method, Forward Feature Selection (FFS) method is used in feature selection to reduce the training time by about 50 to 100 times without loss of performance. Furthermore, hierarchical feature spaces (both local and global) to construct a detector cascade...
This paper presents a novel approach for face detection, which is based on the discriminative MspLBP features selected by a boosting technique called the Ada-LDA method. By scanning the face image with a scalable sub-window, many sub-regions are obtained from which the MspLBP features are extracted to describe the local structures of a face image. From a large pool of the MspLBP features within the...
The training of the adaboost algorithm for face detection is time costly; it often needs days or weeks in the previous system. In this paper, we describe efficient optimization techniques and implement skills 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...
In this paper, a robust and effective face detection method with HTF-Boosting is proposed. Firstly, a new feature, called Haar texture feature, is proposed that has many merits compared with Haar-Like feature. Secondly, a new Boosting algorithm, called Haar Texture Feature Boosting (HTF-Boosting), is proposed to construct strong face/nonface classifiers. The HTF-Boosting algorithm trains strong classifiers...
Face detection in images is very important for many multimedia applications. Haar-like wavelet features have become dominant in face detection because of their tremendous success since Viola and Jones [1] proposed their AdaBoost based detection system. While Haar features' simplicity makes rapid computation possible, its discriminative power is limited. As a consequence, a large training dataset is...
This paper presents a method for object detection based on a cascade of scale and orientation normalized Gaussian derivative classifiers learnt with Adaboost. Normalized Gaussian derivatives provide a small but powerful feature set for rapid learning using Adaboost. Real time detection is made possible by use of a fast integer coefficient algorithm that computes a half-octave Gaussian pyramid with...
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