## 4 Experiments
### Datasets and Protocols  
Seven datasets were used in our experiments. Below are the details and evaluation protocols of the datasets.  
Figure 3: (**a**) Pipeline of SSPP face recognition via enriching intra-variation. (**b**) Pipeline of SSPP face recognition via enriching both intra-variation and invariant features.  
**FERET-b**: FERET-b database [56], collected in a laboratory environment, contains 1400 images of 200 subjects with different pose, expression, and illumination. The SSPP FR experiments on this database were conducted in both verification and identification modes. In identification, we used the neutral and frontal image of each person as gallery and the remaining images as probe. In verification, we divided the database into 10 non-overlapping subsets. Each subset contained 120 positive pairs (i.e., pairs of images from the same person) and 120 negative pairs (i.e., pairs of images from different people).  
**LFW**: Labeled Faces in the Wild (LFW) database [57] includes more than 13,000 images of 5749 different individuals taken under an unconstrained environment. The evaluation protocol suggests dividing the database into 10 non-overlapping subsets. Each subset contains 300 positive pairs and 300 negative pairs. LFW-a is a version of LFW after alignment using commercial software. For the sake of fair comparison, we selected the first 50 people who have more than 10 samples for evaluation according to the experimental setup described in [43]. We randomly selected one sample of each of the 50 subjects as gallery, and the remaining images were used as probe.  
**CPLFW**: CPLFW [58], a renovation of LFW, constructs a cross-pose LFW database to evaluate the influence of pose variation in face recognition. It provides 10 disjoint subsets of image pairs for face verification. Each subset contains 300 positive pairs and 300 negative pairs.  
**CALFW**: Cross-Age LFW (CALFW) database [59], similar to the CPLFW database, is a renovation of LFW. It emphasizes aging effect in addition to other variations (pose, illumination, etc.) in face recognition. The dataset is separated into 10 non-repeating subsets of image pairs for face verification, each subset containing 300 positive pairs and 300 negative pairs.  
**AgeDB**: AgeDB [60] contains 16,488 images of various famous people, such as actors/actresses, writers, scientists, politicians, etc. There are 568 distinct subjects in AgeDB. AgeDB provides 10 folds of image pairs, with each fold consisting of 300 positive pairs and 300 negative pairs.  
**CFP**: CFP database [61], a challenging dataset to examine the problem of frontal to profile face verification, collects 7000 images of 500 subjects with each subject having 10 frontal and 4 profile face images. Its evaluation protocol defines two separate experiments of Frontal-Profile (CFP_FP) and Frontal-Frontal (CFP_FF) face verification, and divides in each experiment the whole dataset into 10 folds each containing 350 positive pairs and 350 negative pairs.  
**VGGFace2-FP**: The VGGFace2 database [62] contains 3.31 million images of 9131 subjects. The dataset is divided into training set and evaluation set, in which the training set contains 8631 subjects and the evaluation set contains 500 subjects. Evaluation scenarios can be divided into two categories by pose and age. We considered the scenario of face matching across different poses, i.e., VGGFace2-FP. The evaluation data are divided into 10 folds of image pairs, with each fold consisting of 250 positive pairs and 250 negative pairs.