Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

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.