Passenger Face Recognition Algorithm for Railway Stations Based on Gabor Wavelet and Manifold Learning
Abstract
To address the low accuracy of traditional station face recognition technology, this paper studies a face recognition algorithm that integrates Gabor wavelet and manifold learning, and constructs a station passenger face recognition system based on this improved algorithm. The algorithm utilizes Gabor wavelet transform to enhance multi-scale and multi-directional image features, employs manifold learning methods such as local linear embedding and Laplacian feature mapping to reduce feature dimensionality, and completes feature extraction and classification through local preservation projection. Results show that on the AR, ORL, and YALE datasets, the algorithm achieves a recognition accuracy of 97%, with an area under the precision-recall curve (AUC) of 0.87. Even with a small number of training samples, it maintains a recognition rate above 93% and a shorter running time, both superior to comparative algorithms. In system application analysis, its mean absolute error is stable between 1.14 and 4, with a maximum absolute percentage error of 5.65%, significantly outperforming traditional LPP and ML-LPP algorithms. The proposed algorithm and system effectively improve station security check efficiency and recognition reliability, providing a new technical solution for public transportation security.References
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