High-order histogram-based local clustering patterns in polar coordinate for facial recognition and retrieval

Abstract

Local feature patterns are conspicuous and are widely used in computer vision, especially in face recognition and retrieval. However, a statistical descriptor that can be used in various scenarios and effectively present the detailed local discrimination information of face images is a challenging and exploring task even if deep learning technology is widelyspread. In this study, we propose a novel local pattern descriptor called the Local Clustering Pattern (LCP) in high-order derivative space for facial recognition and retrieval. Unlike prior methods, LCP exploits the concept of clustering to analyze the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels to encode the local descriptor for facial recognition. There are three tasks (1) Local Clustering Pattern (LCP), (2) Clustering Coding Scheme, (3) High-order Local Clustering Pattern. To generate local clustering pattern, the local derivative variations with multi-direction are considered and that are integrated on rectangular coordinate system with the pairwise combinatorial direction. Moreover, to generate the discriminative local pattern, the features of local derivative variations are transformed from the rectangular coordinate system into the polar coordinate system to generate the characteristics of magnitude (m) and orientation ( $$\theta $$ ). Then, we shift and project the features (m and $$\theta $$ ), which are scattered in the four quadrants of polar coordinate system, into the first quadrant of polar coordinates to strengthen the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels. To encode the local pattern, we consider the spatial relationship between reference and its adjacent pixels and fuse the clustering algorithm into the coding scheme by utilizing the relationship of intra- and inter-classes in a local patch. In addition, we extend the LCP from low- into high-order derivative space to extract the detailed and abundant information for facial description. LCP efficiently encodes the feature of a local region that is discriminative the inter-classes and robust the intra-class of the related pixels to describe a face image. This study has three main contributions: (1) we generate the novel features with magnitude (m) and orientation ( $$\theta $$ ) based on the pairs of the derivative variations to describe the characteristics of each pixel, (2) we shift and project the features from four quadrants of polar coordinate system into the first quadrant of polar coordinates to strengthen the relationship of intra- and inter-classes between pixels in a local patch, (3) we exploit the concept of clustering, which considers the relationship of intra- and inter-classes of the referenced pixel and its adjacent pixels, to encode the local descriptor in a polar coordinate system for facial recognition and retrieval. Experimental results show that LCP outperforms the existing descriptors (LBP, ELBP LDP, LTrP, LVP, LDZP, LGHP) on six public datasets (ORL, Extend Yale B, CAS PEAL, and LFW, CMU-PIE and FERET) for both face recognition and retrieval tasks. Moreover, we further compare the proposed facial descriptor with the popular deep convolutional neural networks to demonstrate the discrimination of the extracted features and applicability of our approach.