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首页> 外文期刊>Journal of Real-Time Image Processing >Modified Viola-Jones algorithm with GPU accelerated training and parallelized skin color filtering-based face detection
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Modified Viola-Jones algorithm with GPU accelerated training and parallelized skin color filtering-based face detection

机译：改进的Viola-Jones算法，具有GPU加速训练和基于皮肤颜色过滤的并行人脸检测

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摘要

Face detection is a prominent research domain in the field of digital image processing. Out of various algorithms developed so far, Viola-Jones face detection has been highly successful. However, because of its complex nature, there is need to do more exploration in its various phases including training as well as actual face detection to find the scope of further improvement in terms of efficiency as well as accuracy under various constraints so as to detect and process the faces in real time. Its training phase for the screening of large amount of Haar features and generation of cascade classifiers is quite tedious and computationally intensive task. Any modification for improvement in its features or cascade classifiers requires re-training of all the features through example images, which are very large in number. Therefore, there is need to enhance the computational efficiency of training process of Viola-Jones face detection algorithm so that further enhancement in this framework is made easy. There are three main contributions in this research work. Firstly, we have achieved a considerable speedup by parallelizing the training as well as detection of rectangular Haar features based upon Viola-Jones framework on GPU. Secondly, the analysis of features selected through AdaBoost has been done, which can give intuitiveness in developing more innovative and efficient techniques for selecting competitive classifiers for the task of face detection, which can further be generalized for any type of object detection. Thirdly, implementation of parallelization techniques of modified version of Viola-Jones face detection algorithm in combination with skin color filtering to reduce the search space has been done. We have been able to achieve considerable reduction in the search space and time cost by using the skin color filtering in conjunction with the Viola-Jones algorithm. Time cost reduction of the order of 54.31% at the image resolution of 640*480 of GPU time versus CPU time has been achieved by the proposed parallelized algorithm.
机译：人脸检测是数字图像处理领域的重要研究领域。在迄今为止开发的各种算法中，Viola-Jones人脸检测已经取得了巨大成功。但是，由于其复杂性，需要在各个阶段进行更多的探索，包括训练和实际面部检测，以找到在各种约束条件下的效率和准确性方面进一步改进的范围，以便进行检测和检测。实时处理面部。其用于筛选大量Haar特征并生成级联分类器的训练阶段是非常繁琐且需要大量计算的任务。为了改进其功能或级联分类器而进行的任何修改都需要通过大量示例图像重新训练所有功能。因此，需要提高Viola-Jones人脸检测算法训练过程的计算效率，以使得在该框架中的进一步增强变得容易。这项研究工作有三个主要贡献。首先，通过在GPU上基于Viola-Jones框架并行化训练和检测矩形Haar特征，我们实现了可观的加速。其次，已经完成了对通过AdaBoost选择的特征的分析，可以直观地开发出更具创新性和效率的技术，以选择竞争性分类器来完成人脸检测任务，并可以进一步推广到任何类型的对象检测。第三，完成了Viola-Jones人脸检测算法的改进版本的并行化技术与皮肤颜色过滤的结合，以减少搜索空间。通过结合使用肤色过滤和Viola-Jones算法，我们已经能够大幅减少搜索空间和时间成本。通过提出的并行算法，在GPU时间为640 * 480的图像分辨率下，CPU的时间成本减少了54.31％。

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