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正面人脸检测方法-神经网络和积极学习算法

摘要

在本案例研究报告中，提出了一种人脸检测方法。人脸检测是人脸识别方法的第一步。人脸检测是Pattern中的一个难点。人脸检测方法主要有基于知识的人脸检测方法、基于特征的人脸检测方法、基于模板的人脸检测方法和基于外观的人脸检测方法。但是在这里我们基本上将人脸检测分为两种方法(i)基于图像的方法(ii)基于特征的方法。我们开发了一个中间系统，使用一种增强算法来训练一个分类器，它能够快速处理图像，同时具有较高的检测率。AdaBoost是一种大裕度分类器，是一种高效的在线学习分类器。为了使AdaBoost算法适应快速人脸识别，将使用所有给定特征的原始AdaBoost与沿特征维度的增强算法进行了比较。结果的可比性保证了后者的使用，其分类速度更快。检测器构建的主要思想是一个基于增强的学习算法:AdaBoost。 AdaBoost is an aggressive learning algorithm which produces a strong classifier by choosing visual features in a family of simple classifiers and combining them linearly. The family of simple classifiers contains simple rectangular wavelets which are reminiscent of the Haar basis. Their simplicity and a new image representation called Integral Image allow a very quick computing of these Haarlike features. Then a structure in cascade is introduced in order to reject quickly the easy to classify background regions and focus on the harder to classify windows. For this, classifiers with an increasingly complexity are combined sequentially. This improves both, the detection speed and the detection efficiency. The detection of faces in input images is proceeded using a scanning window at different scales which permits to detect faces of every size without resampling the original image. On the other hand, the structure of the final classifier allows a realtime implementation of the detector. Due to some limitation of neural network based methods we adopt the Adaboost algorithm for face detection. Here we present some results on real world examples are presented. Our detector found good detection rates with frontal faces and the method can be easily adapted to other object detection tasks by changing the contents of the training dataset.

Sushma Jaiswal, Sarita Singh Bhadauria博士，rakesh Singh Jadon博士

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