This paper presents new image descriptors for heterogeneous face recognition (HFR). The proposed descriptors combine directional and neighborhood information using a rotating spoke and concentric rings concept. We name the descriptors as multi-directional local adjacency descriptors (MDLAD). This family of descriptor captures the directional information through successive rotations of a pair of orthogonal spokes. Likewise, they capture the adjacency information through a comparison against the central pixel of a window with concentric rings around the central pixel. The MDLAD is found to describe the face images well for recognition purposes, which when matched using the chi-squared distance. The face recognition performance with MDLAD improves with its use as a layer in a deep neural network, which yields a robust classification for heterogeneous face recognition with respect to the state-of-the-art methods. The MDLADNET deep network is easily trainable with few hyperparameters and limited data samples as compared to existing similar deep networks. We have experimented on different heterogeneous modalities viz. Extended Yale B, CASIA, CUFSF, IIITD, LFW, Multi-PIE, and CARL, and have found proficient results.

中文翻译：

---

多方向局部邻接描述符（MDLAD），用于异构人脸识别

本文提出了用于异构人脸识别（HFR）的新图像描述符。提出的描述符使用旋转辐条和同心环概念将方向和邻域信息组合在一起。我们将描述符命名为多向本地邻接描述符（MDLAD）。该描述符族通过一对正交轮辐的连续旋转来捕获方向信息。同样，它们通过与围绕中心像素的同心环的窗口中心像素进行比较来捕获邻接信息。发现MDLAD可以很好地描述人脸图像以进行识别，当使用卡方距离进行匹配时，可以识别出该人脸图像。通过将MDLAD用作深度神经网络中的一层，其面部识别性能得以提高，相对于最新方法，该方法可为异类人脸识别提供可靠的分类。与现有的类似深度网络相比，MDLADNET深度网络易于训练，几乎没有超参数和有限的数据样本。我们已经尝试了不同的异构模式，即。扩展的Yale B，CASIA，CUFSF，IIITD，LFW，Multi-PIE和CARL，并已找到熟练的结果。