Designing Security Systems Using Face Recognition Technique

近年来，存在不同级别的安全性，这正在成为信息系统领域的主要问题。当人脸识别被用作安全问题的关键时，使用神经网络仅对少数注册的个人就可以获得良好的结果。然而，随着注册人数的增加，由于人脸信息量巨大，识别率福尔斯下降，训练时间增加。在这项工作中，我们引入了小规模的并行神经网络的模糊理论，以摆脱上述问题。在所提出的方法中，输入图像的模式匹配理论的基础上，首先进行分类到一些类别。然后，利用模糊理论，最接近的模式归属到一些类别。在第二步中，基于小规模神经网络达到关于属于每个类别的图像的最终决策。当一个新的类别被添加到系统中时，它不会影响现有的神经网络，而是创建一个包含新类别的神经网络来附加到预先存在的系统中。这样做不需要重新训练整个网络。小规模神经网络的输入层有256个神经元，隐层有128个神经元，输出层有6个神经元。使用反向传播算法来训练该网络。测试结果（25个注册个人）表明，使用该方法可以获得99.33%的识别率。

Recently, there are different levels of security which are becoming the main problem in the field of information systems. When face recognition is employed as the key in security problem good results can be obtained using neural networks only for a few number of registered individuals. However, with the increase in the registered individuals recognition rate falls and the training time increases because of the huge amount of facial information. In this work, we introduce small scale parallel neurals networks governed by Fuzzy theory to get rid of the aforementioned problem.In the proposed method, the input image is first classified into some categories based on the pattern matching theory. Then, using the Fuzzy theory the closest patterns are attributed into some categories. In the second step, the final decision pertaining to the images belonging to each category is reached based on the small scale neural networks. When a new category is added to the sytem it does not affect the existing neural networks rather a neural network that includes a new category is created to append to the pre-existing system. Doing this there is no need of retraing the whole network. The small scale neural network has 256 neurons in the input layr, 128 neurons in the hidden layr and, 6 neurons in the output layr. The backpropagation algorithm is used to train this network. The test results (for 25 registered individuals) demonstrate that a recognition rate of 99.33% can be obtained using the proposed method.