Facial recognition systems are critical components in numerous applications. They are used, for example, to prevent retail crime, unlock phones, find missing persons, protect law enforcement, and aid forensic investigations. In such real-world applications, the identification of facial information must be both quick and exact. The purpose of this study is to improve both the accuracy and speed of facial recognition. The proposed system reduces overall computational complexity by using a few simple algorithms and transforms. The grayscaling algorithm enhances the image, and the salient features are extracted using a mix of two transform families: the two-dimensional discrete wavelet transform and the two-dimensional discrete cosine transform. This combination exploits the nonorthogonality of the coefficients in both domains to preserve the essential details and perceptual qualities of the original image. A multilayer sigmoid neural network is used for classification since the expensive training stage can be performed offline. The trained network, which uses efficient computations, can be embedded in an online system for rapid classification. The efficiency of the system is an attractive property when processing massive information datasets with limited resources. The recognition system is tested with four freely accessible datasets: the ORL, YALE, FERET-c, and FEI. A test set based on the combination of all datasets is also utilized to evaluate the system performance. Results show that despite the reduction in complexity, the system still maintains high recognition rates as compared to the popular existing methods.

中文翻译：

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使用混合变换和多层 Sigmoid 神经网络分类器的人脸识别系统

面部识别系统是众多应用中的关键组件。例如，它们用于防止零售犯罪、解锁手机、寻找失踪人员、保护执法和协助法医调查。在这样的实际应用中，面部信息的识别必须既快速又准确。这项研究的目的是提高面部识别的准确性和速度。所提出的系统通过使用一些简单的算法和变换来降低整体计算复杂度。灰度算法增强图像，并使用两个变换族的混合提取显着特征：二维离散小波变换和二维离散余弦变换。这种组合利用两个域中系数的非正交性来保留原始图像的基本细节和感知质量。由于昂贵的训练阶段可以离线执行，因此使用多层 sigmoid 神经网络进行分类。经过训练的网络使用高效计算，可以嵌入在线系统中以进行快速分类。在处理资源有限的海量信息数据集时，系统的效率是一个有吸引力的特性。识别系统使用四个可免费访问的数据集进行测试：ORL、YALE、FERET-c 和 FEI。还利用基于所有数据集组合的测试集来评估系统性能。结果表明，尽管降低了复杂性，