Bilateral 2DLDA Program for Face Recognition

Bilateral 2DLDA in face recognition is an enhanced linear discriminant analysis method that incorporates bidirectional projection mechanisms on top of traditional 2DLDA, enabling more effective extraction of discriminative features from facial images.

The implementation approach of this program primarily follows these steps: First, dataset preprocessing is performed, including operations like face alignment and normalization. The code typically implements a data splitting strategy where 9 images per person are randomly selected as the training set and 1 image as the test set. This leave-one-out cross-validation approach allows for better model performance evaluation. Through 20 independent random splits and tests, the average recognition rate is finally taken as the model evaluation metric. This multiple-experiment averaging method effectively reduces randomness introduced by data partitioning, making results more reliable.

The core innovation of bilateral 2DLDA lies in simultaneously considering projection transformations in both row and column directions. The algorithm constructs two projection matrices to maximize the ratio between between-class scatter and within-class scatter. Implementation-wise, this involves solving two eigenvalue problems sequentially. Compared to traditional methods, this bidirectional processing preserves more discriminative information, making it particularly suitable for two-dimensional data structures like facial images. Experimental results demonstrate that this method achieves superior recognition performance on standard face datasets.

At the application level, this algorithm can be deployed in security systems and identity authentication scenarios. Future optimization directions include integrating deep learning features or introducing sparse representations to enhance performance.