Sparse representation classification, as one of the state-of-the-art classification methods,
has been widely studied and successfully applied in face recognition since it was proposed …

The horizontal axis-symmetrical nature of faces is useful and interesting, which has
successfully applied in face detection. In general, images of faces are not strictly captured …

Feature space-based face recognition method performs representation and classification for
face images in the feature space instead of the original space. This representation-based …

The performance and robustness of face recognition are largely determined by the data
samples used for model training. To obtain more representative samples of a face, this …

The collaborative representation-based classification method performs well in the field of
classification of high-dimensional images such as face recognition. It utilizes training …

Recently, collaborative representation based classifiers (CRC) have shown outstanding
performances in recognition tasks. The key to success of most CRC algorithms states that …

In this paper, we propose the negative ε dragging technique for robust classification of noisy
and contaminated data. Different from the naïve ε dragging technique, the negative ε …

Low rank matrix approximation (LRMA) has a wide range of applications in computer vision
and has drawn much attention in recent years. A typical nuclear norm minimization (NNM) is …

Plant recognition is a meaningful research that has attracted many researchers. Due to the
variety of plants, it is difficult for the existing identification methods to identify their species …

Non-negativity is a critical and explainable property in linear representation-based methods
leading to promising performances in the pattern classification field. Based on the non …