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The importance of not being mean: DFM---a norm-referenced data model for face pattern recognition.

机译：不要太刻薄的重要性：DFM ---用于面部模式识别的规范参考数据模型。

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摘要

A successful, mature system for face recognition, Elastic Bunch Graph Matching, represents a human face as a graph in which nodes are labeled with double precision floating-point vectors called "jets". Each jet in a model graph comprises the responses at one fiducial point, or face landmark, of a convolution of the image with a set of self-similar Gabor wavelets of various orientations and spatial scales. Gabor wavelets are scientifically reasonable models for the receptive field profiles of simple cells in early visual cortex. Heretofore, the recognition process simply searched for the stored model graph with the greatest total jet-similarity to a presented image graph. The most widely used measure of jet similarity is the sum over the graph of the dot-products of jets normalized to unit length. We improve significantly upon this system, with orders of magnitude improvements in time and space complexity and marked reductions in recognition error rates. We accomplish these improvements by recasting the concatenated vector of model-graph jets as a binary string, or b-string, comprising bits with one-to-one correspondence to the floating-point coefficients in the model graph. The b-string roughly models a pattern of correlated firing among a population of idealized neurons. The "on" bits of the b-string correspond to the identities of the coefficients that deviate the greatest amount from the corresponding mean coefficient values. We show that this simple recoding consistently reduces recognition error rates by margins exceeding thirty percent. Our investigations support the hypothesis that the b-string representation for faces is extremely efficient and, ultimately, information preserving.
机译：一个成功的成熟的人脸识别系统，Elastic Bunch Graph Matching，将人脸表示为一个图，其中节点用称为“喷射”的双精度浮点矢量标记。模型图中的每个射流都包含在图像的一个基准点或面部界标处的响应，该图像具有一组具有各种方向和空间比例的自相似Gabor小波的卷积。 Gabor小波是科学上合理的模型，用于早期视觉皮层中简单细胞的感受野分布。迄今为止，识别过程仅搜索与所呈现图像图具有最大总射流相似度的存储模型图。射流相似性最广泛使用的度量是标准化为单位长度的射流点积图上的总和。我们对该系统进行了显着改进，时间和空间复杂度提高了几个数量级，识别错误率显着降低。我们通过将模型图射流的连接向量重铸为二进制字符串或b字符串来实现这些改进，二进制字符串或b字符串包含与模型图中的浮点系数一一对应的位。 b字符串大致模拟了一组理想化神经元之间的相关放电模式。 b字符串的“ on”位对应于与相应的平均系数值相差最大的系数的标识。我们表明，这种简单的编码始终将识别错误率降低了百分之三十以上。我们的研究支持以下假设：面部的B字符串表示极其有效，并且最终可以保留信息。

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  • 作者

    Kite, Lawrence Marc.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 143 p.
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:37:52

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