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

SGER：网格到网格的神经网络用于创新的姿态不变的人脸识别这个项目将开发和展示一个全新的模式识别范例的初始构建块，适用于姿态不变的人脸识别。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。这些途径通常从复杂的，巧妙编程和/或手工制作但固定的预处理器或特征提取器开始。在新的范例中，变换是由一类新的实用工具来执行的，这些工具现在可以学习从定义在2D图像网格上的数据到定义在网格上的输出或到全局汇总变量的映射。在所提出的范例中，更强大的图像数据映射将被适配，以最大限度地提高性能的姿态不变的人脸识别任务。该项目将包括利用较新的结构，如蜂窝同步循环网，希望提供更大的转换能力。AT T几年前开发了类似的东西，用于邮政编码数字识别，这是最好的系统，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，如人脸。该项目将尽可能地解决一系列基准挑战，从迷宫穿越问题到2D图像网格中的姿势不变人脸识别。更广泛的重要性：人脸识别是一个对国土安全非常重要的领域，以及其他应用。PI一直在研究仿射变换（如缩放，平移，旋转，杂波等）。在过去的几年里，物体和人脸的不变识别。变换不变人脸识别在各个方面都有着广泛的研究。然而，目前的系统在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。现有的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，一般来说，很难从不同的角度识别人脸。所提出的新的CSRN范式的结果分析的姿态不变的人脸识别问题，以及如何纠正这个问题。预计将导致性能的巨大改进。人们还希望这将揭示人类大脑如何实现这种能力的问题，这反过来对更深入地理解大脑中的学习和智力很重要。

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.