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A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION

机译:一种利用图像配准的人脸识别方法

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This paper presents a technique for face recognition that is based on image registration. The face recognition technique consists of three parts: a training part, an image registration part and a post-processing part. The image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican-hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighborhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images. In the fourth, the transformation parameters between images are obtained using an iterative least squares technique to eliminate outliers.1'2 During training, a set of images are chosen as the training images and the Zernike moments for the feature points of the training images are obtained and stored. The choice of training images depends on the changes of poses and illumination that are expected. In the registration part, the transformation parameters to register the training images with the images under consideration are obtained. In the postprocessing, these transformation parameters are used to determine whether a valid match is found or not. The performance of the proposed method is evaluated using various face databases3^5 and it is compared with the performance of existing techniques. Results indicate that the proposed technique gives excellent results for face recognition in conditions of varying pose, illumination, background and scale.
机译:本文提出了一种基于图像配准的面部识别技术。人脸识别技术包括三个部分:训练部分,图像配准部分和后处理部分。图像配准技术基于在两个图像中找到一组特征点并将这些特征点用于配准。这分四个步骤完成。首先,用墨西哥帽小波对图像进行滤波以获得特征点位置。第二步,在第三步中计算并比较特征点附近的邻域的Zernike矩,以建立两个图像中特征点之间的对应关系。第四,使用迭代最小二乘技术获得图像之间的变换参数,以消除异常值。1'2在训练期间,选择一组图像作为训练图像,并针对训练图像的特征点选择Zernike矩。获取并存储。训练图像的选择取决于预期的姿势和照明的变化。在配准部分中,获得用于将训练图像与所考虑的图像配准的变换参数。在后处理中,这些转换参数用于确定是否找到有效的匹配项。使用各种面部数据库3 ^ 5评估了该方法的性能,并将其与现有技术的性能进行了比较。结果表明,所提出的技术在姿势,照明,背景和比例变化的条件下为面部识别提供了出色的结果。

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