通过神经网络实现人脸识别

中文字数:1481,中文页数:3   英文字数: 672,页数:2

Introduction:
Human face detection is a challenging but interesting problem since the appearance of human faces on digital (still) images strongly depends on the person's emotional state, gender, age, ethnic group and even the quality of the digital devices. The aim of this work is to implement an effective system to locate upright frontal faces on monochromatic images with use of a neural network-based classifier. 
Short description of the system:
The idea of using neural networks in the task of human face detection is derived from the observation that human faces on digital images stand out for a class of well structural objects. There is a number of works applying expert’s knowledge  on some characteristics of human faces (in the form of rules) to locate faces. The drawback of these methods is the need to express expert’s knowledge in an effective way (whatever this term means) and to choose the characteristics which are really important for the detecting process. Neural networks with its ability of learning can be used to extract these characteristics from a set of examples (in this case there are two sets: a set of face patterns -commonly mentioned as the set of positive samples - and the set of nonface patterns – also called negative samples). After the process of learning the structural properties of human faces should be hidden in neural networks in the form of the weights if its connections.

简介：     人脸识别是一项具有挑战性的，但有趣的问题，因为人类面部特征的数字化图像处理强烈依赖于人的情绪状态，性别，年龄，种族，甚至高质量的数字设备。这项工作的目标是落实一个有效的系统，特征提取用于抽取有效特征以降低原模式空间的维数，分类器则根据特征来做出决策分类。神经网络模式识别理论的研究一直是非常活跃的学科。它的发展与神经网络可以说是同步。神经科学作为一门学科开始于19世纪末，意大利解剖学家Golgi在1875年用染色体最先识别出单个神经细胞，并于1889年创立神经元学说。20世纪初，伴随对大脑生理研究的深入，神经科学有了很大的发展，特别是近几十年，神经科学和脑功能研究的发展极为迅速。据专家估计，它将是生物学中继分子生物学研究浪潮后的下一个浪潮。当前，世界上有关神经科学的研究机构、学术活动和出版物相当多，且有迅速增长的趋势。

系统简述：
    人工神经元网络是受人类神经网络系统的启发，利用大量简单处理单元互联而构成的复杂系统，以便于解决一些复杂非线性问题与行为控制问题。构成人工神经元网络的三个基本要素是:神经元、网络拓扑结构和网络训练(学习)方法。