Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval
This work addresses face recognition and retrieval for applications like security and identification, but it is incremental as it builds on existing local descriptor methods.
The paper tackles face recognition and retrieval by proposing a new local descriptor called Local Gradient Hexa Pattern (LGHP), which captures relationships in local neighborhoods and derivative directions to create binary micropatterns, achieving better recognition and retrieval rates compared to state-of-the-art descriptors like LDP and LVP on benchmark datasets such as Cropped Extended Yale-B, CMU-PIE, color-FERET, and LFW.
Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination and lighting conditions. Accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper a local gradient hexa pattern (LGHP) is proposed that identifies the relationship amongst the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. Proposed descriptor effectively transforms these relationships into binary micropatterns discriminating interclass facial images with optimal precision. Recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors namely LDP and LVP over the most challenging and benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, and LFW. The proposed descriptor has better recognition as well as retrieval rates compared to state-of-the-art descriptors.