# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
## 2 Related Works
### Video Face-Recognition Methods for IoT Monitoring Systems  
Video- or image set-based face-recognition has been actively studied for decades. In this paper, we only considered inputs of orderless sets of face images. Since many researchers have pointed out that temporal information is not important for face-recognition tasks, existing methods that exploit temporal dynamics are not considered here. Up until now, existing video recognition or image set classification approaches have been roughly grouped into three classes: model representation-based methods, metric learning-based methods, and simultaneous model representation- and metric learning-based methods. Model representation-based methods mainly focus on how to build more discriminative model representations. For example, the discriminant canonical correlations (DCC) algorithm [14] used subspaces to model videos or image sets, and employed the canonical correlation metric to measure the similarities between two videos. The log-Euclidean metric learning (LEML) algorithm [26] and covariance discriminative learning (CDL) al gorithm [15] both used covariance matrices to model videos and logarithm kernels, in order to learn the distance between videos or image sets. Single Gaussian or Gaussian mixture models (GMM) used probability distributions to model videos, and utilized existing K-L divergence to measure the similarities between the distributions. The disadvantage of model representation-based methods is that they ignore the importance of distance metric learning. Different from model representation-based approaches, metric learning-based methods mainly use existing strategies to represent videos, and focus on how to learn accurate distance metrics. [PERSON] and [PERSON] [27] modeled image sets and videos as affine hulls and convex hulls, and then proposed two metric learning methods: the convex hull-based image set distance (CHISD) algorithm and the affine hull-based image set distance (AHISD) algorithm. They then developed two optimization functions to learn distance metrics, based on collaborative representation. The SANP [28] method also used affine hulls to represent videos, and employs a new distance learning method, which tries to identify the SNAP between two videos. The ISCRC algorithm [19] represents probe videos as regularized/convex hulls and uses all gallery videos to collaboratively reconstruct these hulls in order to learn the discriminative distances. Dual-linear regression classification (DLRC) [16] took LRC as its baseline and extended it to video recognition. Specifically, DLRC used spanning subspaces to represent image sets and videos, and tried to learn more suitable set-based distances. Inspired by DLRC, the concept of unrelated sets was first defined in PLRC, which learned related distance metrics, unrelated distance metrics, and combination metrics to improve classification performance. Unfortunately, experiments have shown that even though DLRC and PLRC can achieve satisfactory performances in small-size video recognition or image set classification tasks, they do not perform well on large-size videos. Additionally, the existing unrelated set construction strategies (i.e., S1 and S2 in PLRC) are not optimal. So, how to construct more discriminative unrelated sets and extend the methods to large-size video recognition tasks remain challenging problems. [PERSON] et al. proposed the KPLRC algorithm, which uses two new, unrelated set-constructing strategies, and tries to increase the dimensionality of the image samples in each image set to overcome the limitations of PLRC. Unfortunately, KPLRC not only increases the classification accuracy but also the additional computational overhead. Inspired by this, a novel SRbPLRC framework is proposed in this paper, which combines the two new unrelated set construction strategies from KPLRC with three extensions of SRbPLRC, for large-size video recognition or image set classification.