FERET

1. Corrected eye coordinates

We have found that there were slight errors on the eye coordinates of the standard FERET distribution and remarked the eye coordinates of all the FERET images accurately. [download the corrected coordinates][download the corresponding manually cropped images] [password: 23e4]

[1] Weihong Deng, Jiani Hu, Jun Guo, "Extended SRC: Undersampled Face Recognition via Intra-Class Variant Dictionary," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 34, no. 9, pp. 1864-1870, 2012

[2] Weihong Deng, Jiani Hu, Jun Guo, In Defense of Sparsity Based Face Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

[3] Weihong Deng, Jiani Hu, Jun Guo, Weidong Cai, Dagan Feng, "Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach", Pattern Recognition, vol. 43. no. 5, pp. 1748-1762, 2010.

 

2. TIPCA aligned images

We develop a transform-invariant PCA (TIPCA) technique which aims to accurately characterize the intrinsic structures of the human face that are invariant to the in-plane transformations of the training images. Specially, TIPCA alternately aligns the image ensemble and creates the optimal eigenspace, with the objective to minimize the mean square error between the aligned images and their reconstructions. The learning from the FERET facial image ensemble of 1,196 subjects validates the mutual promotion between image alignment and eigenspace representation, which eventually leads to the optimized coding and recognition performance that surpasses the handcrafted alignment based on facial landmarks.

[download the aligned images] [password: f00b]

[1] Weihong Deng, Jiani Hu, Jiwen Lu, Jun Guo, Transform-Invariant PCA: A Unified Approach to Fully Automatic Face Alignment, Representation, and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 6, pp. 1275–1284, 2014.