# L1-(2D)2PCANet: A Deep Learning Network for Face Recognition

arXiv: https://arxiv.org/abs/1805.10476 · HTML: https://mlsanity.com/paper/1805.10476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2018.

## Built on by (most cited first, 1 of 1)

- [Monkeypox disease recognition model based on improved SE-InceptionV3](https://mlsanity.com/paper/2403.10087/) (2024)

## Abstract (arXiv)

In this paper, we propose a novel deep learning network L1-(2D)2PCANet for face recognition, which is based on L1-norm-based two-directional two-dimensional principal component analysis (L1-(2D)2PCA). In our network, the role of L1-(2D)2PCA is to learn the filters of multiple convolution layers. After the convolution layers, we deploy binary hashing and block-wise histogram for pooling. We test our network on some benchmark facial datasets YALE, AR, Extended Yale B, LFW-a and FERET with CNN, PCANet, 2DPCANet and L1-PCANet as comparison. The results show that the recognition performance of L1-(2D)2PCANet in all tests is better than baseline networks, especially when there are outliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust to outliers and changes of the training images.