Solving Face Recognition Problems Using Wavelet Neural Networks

Wavelet Neural Networks can be effectively applied to solve face recognition problems, demonstrating excellent practicality and performance. This integrated model combines wavelet transform theory with neural network algorithms, implementing feature extraction through multi-level wavelet decomposition of facial images followed by neural network training. The typical implementation involves preprocessing face images, applying discrete wavelet transforms (DWT) to extract frequency-domain features, and then using neural networks (such as multilayer perceptrons) for classification. Key functions in implementation include wavelet coefficient calculation, feature dimension reduction, and similarity matching against known face databases. This approach achieves accurate face recognition by transforming facial patterns into wavelet-domain representations and learning discriminative features through backpropagation training. The method finds applications in various scenarios including face-based access control systems, facial payment verification systems, and intelligent surveillance systems. Therefore, Wavelet Neural Networks show extensive application prospects and significant potential in the field of face recognition technology.