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首页> 外文会议>Intelligent robots and computer vision XXXI: algorithms and techniques >A Viola-Jones based hybrid face detection framework
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A Viola-Jones based hybrid face detection framework

机译：基于Viola-Jones的混合人脸检测框架

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摘要

Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.
机译：人脸检测性能的提高将使许多应用受益。 OpenCV库实现了标准解决方案Viola-Jones检测器，并具有统计上提高的二进制分类器的拒绝级联。经验证据表明，Viola-Jones在某些情况下检测不足。这项研究表明，由神经网络增强的截断级联可以恢复这些未被检测到的面孔。构建了一个混合框架，其中包含截短的Viola-Jones级联和一个人工神经网络，用于改善人脸决策。最佳地，选择截断阶段，该截断阶段捕获了所有面部并允许神经网络删除虚假警报。训练具有一个隐藏层的前馈反向传播网络，以基于完全拒绝级联的中间阶段的阈值（检测）值来区分人脸。聚类算法被用作神经网络的先驱，以对明显的重叠进行分组。在CMU / VASC图像数据库上进行了评估，与未经修改的OpenCV方法进行的比较显示：（1）如果不增加虚警的要求，检测率提高37％；（2）如果不增加错误警报，则检测率提高48％。一些额外的虚假警报是可以容忍的；（3）虚假警报减少了82％，检测率没有降低。这些结果证明了改进的面部检测，并可以满足在各种应用中进行此类改进的需求。

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  • 来源
  • 会议地点 San Francisco CA(US)
  • 作者

    Thomas M. Murphy; Randy Broussard; Robert Schultz; Ryan Rakvic; Hau Ngo;

  • 作者单位

    Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, Maryland, USA;

    Center for Biometric Signal Processing, Department of Weapons and Systems Engineering, United States Naval Academy, Annapolis, Maryland, USA;

    Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, Maryland, USA;

    Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, Maryland, USA;

    Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, Maryland, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    face detection; Viola-Jones; neural network; hybrid; CMU/VASC;

    机译：人脸检测中提琴-琼斯神经网络;杂种CMU / VASC;

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