Image-to-image face recognition using Dual Linear Regression based Classification and Electoral College voting

This paper proposes an image-to-image face recognition algorithm that uses Dual Linear Regression based Classification (DLRC) and an Electoral College voting approach. Each face image involved is first converted into a cluster of images; each image in the cluster is obtained by shifting the original image a few pixels. The similarity of a pair of face images can be measured by comparing the distance between the corresponding image clusters, which is calculated using DLRC approach. To further improve performance, each cluster of images, representing a single face image, is then partitioned into a union of clusters of sub images. DLRC is then used to measure similarities between corresponding sub-image clusters to provide temporary identity decisions; a voting approach is applied to make final conclusions. We have carried out experiments on a benchmark dataset for face recognition. The result demonstrates that the proposed approach works best in certain simple situations, while its performance is also comparable to known algorithms in complicated situations.