SGER: Grid-to-grid neural networks for innovative pose invariant face recognition

SGER：用于创新姿态不变人脸识别的网格到网格神经网络该项目将开发并演示适用于姿态不变人脸识别的全新模式识别范例的初始构建块。智力优势：传统的模式识别系统开发或训练固定的计算路径，从原始输入到最终的分类或描述。这些路径通常是从复杂的、巧妙编程的和/或手工制作的，但固定的预处理器或特征提取器开始的。在新的范例中，转换由一类新的实用工具执行，这些工具可以学习从2D图像网格上定义的数据到网格上定义的输出或全局汇总变量的映射。在提出的范式中，将采用更强大的图像数据映射，以最大限度地提高姿态不变人脸识别任务的性能。该项目将包括开发新的结构，如蜂窝同步循环网络（CSRN），希望提供更大的转换能力。at&t在几年前开发了类似的东西，用于邮政编码数字识别，这是目前最适合这项任务的系统，但它只能进行前馈分析，这使得它不适合处理更复杂的图像，比如人脸。该项目将尽可能解决一系列基准挑战，从迷宫穿越问题到二维图像网格中的不变性人脸识别。更广泛的影响：在其他应用中，人脸识别对国土安全非常重要。在过去的几年里，PI一直在研究物体和人脸的仿射变换（如缩放、平移、旋转、杂波等）不变识别。变换不变人脸识别在各个方面都得到了广泛的研究。然而，目前的系统在许多个月后识别同一张脸时通常非常不准确——这是一个非常重要的实际问题。现有人脸识别技术的另一个关键缺点是，如果我们只从一个视角看到人脸，通常很难从不同的角度识别人脸。基于对姿态不变人脸识别问题的分析，提出了一种新的CSRN范式。可以预期，性能将会有很大的提高。人们还希望这将阐明人类大脑是如何实现这种能力的问题，这对深入了解大脑的学习和智力是很重要的。

SGER: Grid-to-grid neural networks for innovative pose invariant face recognitionThis project will develop and demonstrate the initial building blocks for a whole new paradigm for pattern recognition, applicable to pose invariant face recognition.INTELLECTUAL MERIT: Conventional pattern recognition systems develop or train fixed pathways of computation, from the raw input to the final classification or description. These pathways usually start out from complex, cleverly programmed and/or hand crafted but fixed preprocessors or feature extractors. In the new paradigm, the transforms are performed by a new class of practical tools now available which learn mappings from data defined over a 2D image grid to outputs defined over a grid or to global summary variables. In the proposed paradigm, more powerful mapping of image data will be adapted so as to maximize performance in the pose invariant face recognition task. This project will include exploitation of newer structures such as the cellular simultaneous recurrent net (CSRN), which are hoped to provide greater transformational capability. AT&T developed something similar years ago, for ZIP code digit recognition, which was the best system available for that task -- but it was only able to do feed forward analysis, which made it unsuitable for handling more complex images such as faces. This project will go as far as possible to address a series of benchmark challenges, from the maze traversing problem to pose invariant face recognition in 2D image grid. BROADER IMPACTS: Face recognition is an area of great importance to homeland security, among other applications. The PI has been investigating affine transformation (such as scale, translation, rotation, clutter etc.) invariant recognition in objects and faces for last several years. There has been intense research in different aspects of transformation invariant face recognition. However, current systems are typically very inaccurate in recognizing the same face after many months -- an issue of great practical importance. Another critical shortcoming of existing face recognition techniques is that if we have seen faces only from one viewing angle, in general, it is difficult to recognize the faces from disparate angles. The proposed novel CSRN paradigm results from analysis of the pose invariant face recognition problem and of how to correct the problem. It is anticipated that a quantum improvement in performs a will result. It is also hoped that this will shed light on the question of how the human brain achieves such capabilities, which is important in turn to a deeper understanding of learning and intelligence in the brain.