A Robust Fused Descriptor Under Unconstrained Conditions

Karanwal, Shekhar; Roka, Sanjay

doi:10.1007/978-981-16-4538-9_35

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…PMDMC [18] and LSDA [18] gains the ACC of 91.54% and 85.38% on Trg=3. GFST [19], QRCP-based [19], GBSBP [20], GBSBP+LPQ [20], R&L_ESRC [21], H&L_ESRC [21], OD-LBP [22], OS-LTP [22] and LDBP [23] secure the ACC of 76.67%, 68.89%, 74.44%, 78.05%, 77.22%, 75.72%, 82.77%, 67.77% and 75.20% on Trg=1. The proposed NABILD secures better ACC than all the 13 compared literature methods.…”

Section: Accuracy Comparison With Others (Literature Ones)mentioning

confidence: 99%

NABILD: Noise And Blur Invariant Local Descriptor for Face Recognition

Karanwal

Diwakar²

2022

J. Phys.: Conf. Ser.

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Most of the existing local descriptors unable to perform well in front of noise and blur variations. Additionally there are very less descriptors persist in literature which are noise and blur invariant. To remedy this challenge the proposed work launch novel descriptor under noise and blur changes so-called Noise and Blur Invariant Local Descriptor (NABILD). With respect to two artificial noises i.e. Gaussian White Noise (GWN) and Salt & Pepper Noise (SPN) with artificial image blurring, the NABILD is introduced. Precisely NABILD takes essentials of two well performed descriptors with respect to noise and blur variations. The first one is Median Robust Extended LBP based on Neighborhood Intensity (MRELBP-NI) and second one is Multiscale Local Phase Quantization (MLPQ). MRELBP-NI is very effective in controlling GWN and SPN due to the capturing of microstructure and macrostructure information. LPQ is the efficient blur invariant descriptor as it quantizes the phase. By considering the merits of both of these descriptors their features are integrated into one framework called as NABILD. To lower down the feature dimension FLDA is deployed and classification is conducted by SVMs. Experiments on ORL face dataset confirm strength of NABILD against other tested descriptors in noise and blur variations. Various literature methods are also outclassed by NABILD.

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