SGER: Popularizing Neural Processes: A Project to Place anOptoelectronic Neural System in every Wallet

SGER：普及神经过程：将光电神经系统放入每个钱包的项目

• 批准号: 9617121
• 负责人: Bahram Javidi
• 金额: $ 5万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 1999-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617121&HistoricalAwards=false
• 关键词: SGER; Popularizing; Neural; Processes; Project

9617121 Javidi Artificial neural networks mimic biological neural systems and are characterized by massively interconnected processing elements called neurons. The processing is performed by training and adjusting the strength of the interconnection between neurons. Neural network systems are ideal for solving problems that are difficult to describe mathematically. Even though neural networks hold a great promise for solving complex computational problems, they have not been utilized extensively in day-to-day applications, and their application has been limited mainly to military projects. Widespread commercialization of neural network would greatly benefit the field and would make available more funds for R&D. Development of a practical, low-cost optoelectronic neural system that would take advantage of the computational potential of neural networks would facilitate further commercialization of both neural network and optoelectronic technology. Recent research has discovered new hardware and software for an application for face recognition and other biometrics that is both uniquely suited to exploit the characteristics of neural processing and has the potential for widespread commercialization. This proposal aims to develop a compact, low-cost optoelectronic processor to implement a neural system for face recognition. Face recognition and classification is a difficult task because the facial appearance is constantly changing due to different head perspectives, facial expressions, different illuminations, hair styles, etc. The face recognition problem requires storing a large data base of facial features to successfully implement the massive interconnection between the neurons required to classify facial images. The proposed system can use optical recording materials such as photopolymers to store the large facial data base for an individual on an ID card, such as a driver's license. For inspection, a live image of the person carrying the card is displayed on an optoelectronic device and is compared to the facial features stored in the photopolymer film on the card. This comparison is performed by an optoelectronic neural chip which carries out the computation necessary for an accurate classification. For additional security, the facial features can be optically encrypted by phase encoding to prevent reproduction of the ID cards by unauthorized people 5 . One of the advantages of the proposed system is that using optical materials, large amounts of facial information can be stored on a relatively small area, fitting easily on the card. Also, the facial information is read out in parallel by light beams which can perform parallel computation on large arrays of data, and therefore the recognition can be performed in a short period of time. Neural algorithms will be developed to provide reliable face recognition methods. Challenges are in the area of producing very low probability of error algorithms, low-cost input-output devices to display the information, and compact optical systems architectures and designs. We propose to use a nonlinear filter based optoelectronics neural networks associated with a supervised perception learning algorithm for real-time face recognition. The first layer is implemented optically using a nonlinear joint transform correlator (JTC) 2-4 and the second layer is implemented electronically because of the small number of the hidden layer neurons. The system is trained with a sequence of input facial images and is able to classify an input face in real-time. Based on the characteristics of the nonlinear JTC, the proposed system has the following features: it is easy to implement optically and is robust in terms of system alignment; the system can be integrated into a low-cost compact prototype; the system is trained by updating the reference images (weights)in the input which can be stored in electronic or optical memories and no filters or holograms need to be produced; using nonlinear transformation in the Fourier plane, the system is robust to ill umination variations of the input image, has a good discrimination sensitivity and is robust to noise; and the system is shift invariant. The processor may use commercially available opto-electronics devices and can be built as a low cost compact system. Computer simulations and optical experimental results will be performed to determine the probability of error of the system in identifying input facial images. By using time multiplexing of the input image under investigation, we hope that using more than one input image, the probability of error for classification can be reduced to zero. ***

9617121 Javidi人工神经网络模仿生物神经系统，其特征是大量互连的处理元件，称为神经元。处理是通过训练和调整神经元之间的互连强度来执行的。神经网络系统是解决难以用数学描述的问题的理想选择。尽管神经网络在解决复杂的计算问题方面有很大的潜力，但它们在日常应用中还没有得到广泛的利用，而且它们的应用主要限于军事项目。神经网络的广泛商业化将使该领域受益匪浅，并将为研发提供更多的资金。开发一种实用的、低成本的光电神经系统，利用神经网络的计算潜力，将促进神经网络和光电技术的进一步商业化。最近的研究发现了用于人脸识别和其他生物识别应用的新硬件和软件，这些硬件和软件都非常适合利用神经处理的特性，并具有广泛商业化的潜力。 该提案旨在开发一种紧凑，低成本的光电处理器，以实现用于人脸识别的神经系统。人脸识别和分类是一项艰巨的任务，因为面部外观是不断变化的，由于不同的头部角度，面部表情，不同的光照，发型等人脸识别问题需要存储一个庞大的数据库的面部特征，成功地实现大量的神经元之间的互联需要分类的面部图像。拟议的系统可以使用光聚合物等光学记录材料将个人的大型面部数据库存储在身份证（例如驾驶执照）上。为了进行检查，携带卡片的人的实时图像显示在光电设备上，并与存储在卡片上的光聚合物膜中的面部特征进行比较。这种比较由光电神经芯片执行，该芯片执行精确分类所需的计算。为了增加安全性，面部特征可以通过相位编码进行光学加密，以防止未经授权的人复制ID卡5。所提出的系统的优点之一是，使用光学材料，大量的面部信息可以存储在一个相对较小的区域，很容易安装在卡上。此外，通过能够对大数据阵列执行并行计算的光束来并行读出面部信息，因此能够在短时间段内执行识别。将开发神经算法以提供可靠的人脸识别方法。挑战是在产生非常低的错误算法的概率，低成本的输入输出设备显示的信息，和紧凑的光学系统架构和设计的领域。 我们建议使用一个非线性滤波器为基础的光电神经网络与监督感知学习算法的实时人脸识别。第一层使用非线性联合变换相关器（JTC）2-4光学地实现，并且第二层由于隐藏层神经元的数量少而电子地实现。该系统使用一系列输入面部图像进行训练，并且能够实时对输入面部进行分类。基于非线性联合变换器的特点，该系统具有以下特点：易于光学实现，系统对准稳定，系统可集成为低成本的小型样机，系统通过更新输入中的参考图像（权值）进行训练，这些参考图像可存储在电子或光学存储器中，不需要制作滤波器或全息图;利用傅立叶平面内的非线性变换，系统对输入图像的光照变化具有鲁棒性，具有良好的分辨灵敏度，对噪声具有鲁棒性，并且系统具有平移不变性。处理器可以使用商业上可获得的光电子器件，并且可以被构建为低成本紧凑系统。将进行计算机模拟和光学实验结果，以确定系统在识别输入的面部图像的错误的概率。通过使用时间复用的输入图像的调查，我们希望使用一个以上的输入图像，分类错误的概率可以减少到零。 ***