一种复杂场景中鲁棒和精确的面部特征检测方法

S. Duffner, Christophe Garcia
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引用次数: 33

摘要

我们提出了一种鲁棒自动检测图像中用户选择的一组面部特征的技术,如瞳孔、鼻尖、嘴巴中心等。该系统基于异构神经层的特定架构,从带有注释面部特征的人脸训练集中自动合成简单的特定问题特征提取器和分类器。经过训练后,人脸特征检测系统就像一个简单的过滤器管道,将原始输入的人脸图像作为一个整体来处理,并构建全局人脸特征映射,通过简单的全局最大值搜索可以很容易地检索到面部特征位置。我们的实验表明,我们的方法对光照和姿态变化以及噪声和部分遮挡都非常稳健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A connexionist approach for robust and precise facial feature detection in complex scenes
We present a technique for robustly and automatically detect a set of user-selected facial features in images, like the eye pupils, the tip of the nose, the mouth centre, etc. Based on a specific architecture of heterogeneous neural layers, the proposed system automatically synthesises simple problem-specific feature extractors and classifiers from a training set of faces with annotated facial features. After training, the facial feature detection system acts like a pipeline of simple filters that treats the raw input face image as a whole and builds global facial feature maps, where facial feature positions can easily be retrieved by a simple search for global maxima. We experimentally show that our method is very robust to lighting and pose variations as well as noise and partial occlusions.
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