# Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval

arXiv: https://arxiv.org/abs/2201.01275 · HTML: https://mlsanity.com/paper/2201.01275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 44 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [A Deeper Look at Dataset Bias](https://mlsanity.com/paper/1505.01257/) (2015)

## Abstract (arXiv)

In this paper a novel hand crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. Most of the existing hand-crafted descriptors encodes only a limited number of pixels in the local neighbourhood. Under unconstrained environment the performance of these descriptors tends to degrade drastically. The major problem in increasing the local neighbourhood is that, it also increases the feature length of the descriptor. The proposed descriptor try to overcome these problems by defining an efficient encoding structure with optimal feature length. The proposed descriptor encodes relations amongst the neighbours in quadruple space. Two micro patterns are computed from the local relationships to form the descriptor. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases namely; Caltech-face, LFW, Colour-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under uncontrolled variations in pose, illumination, background and expressions.