In this paper, a novel class-specific kernel linear regression classification is proposed for face recognition under very low-resolution and severe illumination variation conditions. Since the low-resolution problem coupled with illumination variations makes ill-posed data distribution, the nonlinear projection rendered by a kernel function would enhance the modeling capability of linear regression for the ill-posed data distribution. The explicit knowledge of the nonlinear mapping function can be avoided by using the kernel trick. To reduce nonlinear redundancy, the low-rank-r approximation is suggested to make the kernel projection be feasible for classification. With the proposed class-specific kernel projection combined with linear regression classification, the class label can be determined by calculating the minimum projection error. Experiments on 8 × 8 and 8 × 6 images down-sampled from extended Yale B, FERET, and AR facial databases revealed that the proposed algorithm outperforms the state-of-the-art methods under severe illumination variation and very low-resolution conditions.

Stereo matching of two distanced cameras and structured-light RGB-D cameras are the two common ways to capture the depth map, which conveys the per-pixel depth information of the image. However, the results with mismatched and occluded pixels would not provide accurately well-matched depth and image information. The mismatched depth-image relations would degrade the performances of view syntheses seriously in modern-day three-dimension video applications. Therefore, how to effectively utilize the image and depth to enhance themselves becomes more and more important. In this paper, we propose an advanced multilateral filter (AMF), which refers spatial, range, depth, and credibility information to achieve their enhancements. The AMF enhancements could sharpen the image, suppress noisy depth, filling depth holes, and sharpen the depth edges simultaneously. Experimental results demonstrate that the proposed method provides a superior performance, especially around the object boundary.

Owing to losing the detailed information, the low-resolution problem in face recognition degrades the recognition performance dramatically. To overcome this problem, a novel face-recognition system has been proposed, consisting of the extracted feature vectors from the multiple-size discrete cosine transforms (mDCTs) and the recognition mechanism with selective Gaussian mixture models (sGMMs). The mDCT could extract enough visual features from low-resolution face images while the sGMM could exclude unreliable observation features in recognition phase. Thus, the mDCT and the sGMM can greatly improve recognition rate at low-resolution conditions. Experiments are carried out on George Tech and AR facial databases in 16 × 16 and 12 × 12 pixels resolution. The results show that the proposed system achieves better performance than the existing methods for low-resolution face recognition.

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.