Kou Kit Ian
IEEE Trans Image Process. 2016 Jul;25(7):3287-3302. doi: 10.1109/TIP.2016.2567077. Epub 2016 May 11.
Collaborative representation-based classification (CRC) and sparse RC (SRC) have recently achieved great success in face recognition (FR). Previous CRC and SRC are originally designed in the real setting for grayscale image-based FR. They separately represent the color channels of a query color image and ignore the structural correlation information among the color channels. To remedy this limitation, in this paper, we propose two novel RC methods for color FR, namely, quaternion CRC (QCRC) and quaternion SRC (QSRC) using quaternion ℓ minimization. By modeling each color image as a quaternionic signal, they naturally preserve the color structures of both query and gallery color images while uniformly coding the query channel images in a holistic manner. Despite the empirical success of CRC and SRC on FR, a few theoretical results are developed to guarantee their effectiveness. Another purpose of this paper is to establish the theoretical guarantee for QCRC and QSRC under mild conditions. Comparisons with competing methods on benchmark real-world databases consistently show the superiority of the proposed methods for both color FR and reconstruction.
基于协作表示的分类（CRC）和稀疏表示分类（SRC）最近在人脸识别（FR）领域取得了巨大成功。先前的CRC和SRC最初是在基于灰度图像的FR实际场景中设计的。它们分别表示查询彩色图像的颜色通道，而忽略了颜色通道之间的结构相关信息。为了弥补这一局限性，在本文中，我们提出了两种用于彩色FR的新颖表示分类方法，即使用四元数ℓ最小化的四元数CRC（QCRC）和四元数SRC（QSRC）。通过将每个彩色图像建模为四元数信号，它们自然地保留了查询和图库彩色图像的颜色结构，同时以整体方式统一编码查询通道图像。尽管CRC和SRC在FR上取得了经验性成功，但为保证其有效性而得出的理论结果却很少。本文的另一个目的是在温和条件下为QCRC和QSRC建立理论保证。在基准真实世界数据库上与竞争方法的比较一致地表明了所提出方法在彩色FR和重建方面均具有优越性。
