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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching
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Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching

机译：基于MRF模型图像匹配的姿态不变人脸识别能量归一化

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摘要

A pose-invariant face recognition system based on an image matching method formulated on MRFs is presented. The method uses the energy of the established match between a pair of images as a measure of goodness-of-match. The method can tolerate moderate global spatial transformations between the gallery and the test images and alleviate the need for geometric preprocessing of facial images by encapsulating a registration step as part of the system. It requires no training on nonfrontal face images. A number of innovations, such as a dynamic block size and block shape adaptation, as well as label pruning and error prewhitening measures have been introduced to increase the effectiveness of the approach. The experimental evaluation of the method is performed on two publicly available databases. First, the method is tested on the rotation shots of the XM2VTS data set in a verification scenario. Next, the evaluation is conducted in an identification scenario on the CMU-PIE database. The method compares favorably with the existing 2D or 3D generative model-based methods on both databases in both identification and verification scenarios.
机译：提出了一种基于基于MRF的图像匹配方法的姿态不变人脸识别系统。该方法使用一对图像之间建立的匹配的能量作为匹配优度的量度。该方法可以通过封装配准步骤作为系统的一部分，来容忍画廊和测试图像之间的适度全局空间变换，并减轻对面部图像进行几何预处理的需要。它不需要对非正面人脸图像进行训练。引入了许多创新，例如动态块大小和块形状适应性，以及标签修剪和错误预增白措施，以提高该方法的有效性。该方法的实验评估是在两个公共数据库上进行的。首先，在验证方案中对XM2VTS数据集的旋转镜头进行测试。接下来，在CMU-PIE数据库的识别方案中进行评估。在识别和验证场景中，该方法均优于两个数据库上现有的基于2D或3D生成模型的方法。

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