Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

• URL: http://arxiv.org/abs/2201.00509v1
• Date: Mon, 3 Jan 2022 07:45:36 GMT
• Title: Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval
• Authors: Soumendu Chakraborty, Satish Kumar Singh, and Pavan Chakraborty
• Abstract summary: A local gradient hexa pattern (LGHP) is proposed that identifies the relationship amongst the reference pixel and its neighboring pixels. Discriminative information exists in the local neighborhood as well as in different derivative directions. The proposed descriptor has better recognition as well as retrieval rates compared to state-of-the-art descriptors.
• Score: 20.77994516381
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Residual Learning for Image Point Descriptors [56.917951170421894]
  We propose a very simple and effective approach to learning local image descriptors by using a hand-crafted detector and descriptor. We optimize the final descriptor by leveraging the knowledge already present in the handcrafted descriptor. Our approach has potential applications in ensemble learning and learning with non-differentiable functions.
  arXiv  Detail & Related papers  (2023-12-24T12:51:30Z)
• Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors [101.62384271200169]
  A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks. We investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval.
  arXiv  Detail & Related papers  (2022-09-27T17:59:04Z)
• Local Directional Gradient Pattern: A Local Descriptor for Face Recognition [20.77994516381]
  The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions. Results of the experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate is almost similar to the existing state of the art methods.
  arXiv  Detail & Related papers  (2022-01-03T08:34:25Z)
• Cascaded Asymmetric Local Pattern: A Novel Descriptor for Unconstrained Facial Image Recognition and Retrieval [20.77994516381]
  In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image. The proposed encoding scheme has optimum feature length and shows significant improvement in accuracy under environmental and physiological changes in a facial image.
  arXiv  Detail & Related papers  (2022-01-03T08:23:38Z)
• Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval [20.77994516381]
  A novel hand crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. The proposed descriptor encodes relations amongst the neighbours in quadruple space. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases.
  arXiv  Detail & Related papers  (2022-01-03T08:04:38Z)
• Centre Symmetric Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval [20.77994516381]
  Hand-crafted descriptors identify the relationships of the pixels in the local neighbourhood defined by the kernel. In this paper we propose a hand-crafted descriptor namely Centre Symmetric Quadruple Pattern (CSQP), which encodes the facial asymmetry in quadruple space. Result analysis shows that the proposed descriptor performs well under controlled as well as uncontrolled variations in pose, illumination, background and expressions.
  arXiv  Detail & Related papers  (2022-01-03T07:56:24Z)
• R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval [20.77994516381]
  R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. Proposed encoding scheme divides the local neighborhood into sectors of equal angular width. Average grayscales values of these two subsectors are encoded to generate the micropatterns.
  arXiv  Detail & Related papers  (2022-01-03T07:39:23Z)
• Learning Fair Face Representation With Progressive Cross Transformer [79.73754444296213]
  We propose a progressive cross transformer (PCT) method for fair face recognition. We show that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance.
  arXiv  Detail & Related papers  (2021-08-11T01:31:14Z)
• Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
  We propose a unified framework calledInter-class DiscrepancyAlignment(IDA) IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors. IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
  arXiv  Detail & Related papers  (2021-03-02T08:20:08Z)
• Blind Face Restoration via Deep Multi-scale Component Dictionaries [75.02640809505277]
  We propose a deep face dictionary network (termed as DFDNet) to guide the restoration process of degraded observations. DFDNet generates deep dictionaries for perceptually significant face components from high-quality images. component AdaIN is leveraged to eliminate the style diversity between the input and dictionary features.
  arXiv  Detail & Related papers  (2020-08-02T07:02:07Z)
• Online Invariance Selection for Local Feature Descriptors [93.32949876169785]
  A limitation of current feature descriptors is the trade-off between generalization and discriminative power. We propose to overcome this limitation with a disentanglement of invariance in local descriptors and with an online selection of the most appropriate invariance given the context. We demonstrate that our method can boost the performance of current descriptors and outperforms state-of-the-art descriptors in several matching tasks.
  arXiv  Detail & Related papers  (2020-07-17T14:08:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.