Identity recognition based on generalised linear regression classification for multi‐component images

Abstract

In real‐world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library‐100, SOIL‐47, SDUMLA‐HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi‐component identity recognition.

Advances of Robust Subspace Face Recognition

“…Ridge regression (RR) [10] estimated the regression parameters by using a regularized least square method to model the linear dependency in the spatial domain. Huang et al and Chou et al presented several improved approaches of LRC, including improved-PCA-LRC [11], LDA-LRC [12], unitary-LRC [13], and generalized-LRC [14,15] for dealing with different situations like facial expressions, lighting changes, and pose variations. Lai et al [16] utilized the least trimmed square (LTS) as a robust estimator to detect the contaminated pixels from query for boosting the performance under the partial occlusion situation.…”