Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor

Koley, Subhadeep; Roy, Hiranmoy; Bhattacharjee, Debotosh

doi:10.1016/j.patrec.2021.01.028

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Nevertheless, the absolute accuracy in the sketch to photo recognition is low. Several methods in literature have shown (on a different protocol) that the sketch recognition performance could be improved with specifically designed features [68] and specially designed neural network models [69]. The sketch modality is very different compared to other heterogeneous imaging modalities we have considered so far like thermal, nearinfrared, and so on.…”

Section: ) Experiments With Arl-vtf Datasetmentioning

confidence: 99%

Prepended Domain Transformer: Heterogeneous Face Recognition Without Bells and Whistles

George

Mohammadi

Marcel

2023

IEEE Trans.Inform.Forensic Secur.

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Heterogeneous Face Recognition (HFR) refers to matching face images captured in different domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared to visible, and so on. This is particularly useful in matching visible spectrum images to images captured from other modalities. Though highly useful, HFR is challenging because of the domain gap between the source and target domain. Often, large-scale paired heterogeneous face image datasets are absent, preventing training models specifically for the heterogeneous task. In this work, we propose a surprisingly simple, yet, very effective method for matching face images across different sensing modalities. The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap. Retraining this new block with few paired samples in a contrastive learning setup was enough to achieve stateof-the-art performance in many HFR benchmarks. The PDT blocks can be retrained for several source-target combinations using the proposed general framework. The proposed approach is architecture agnostic, meaning they can be added to any pre-trained FR models. Further, the approach is modular and the new block can be trained with a minimal set of paired samples, making it much easier for practical deployment. The source code and protocols will be made available publicly.

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Section: ) Experiments With Arl-vtf Datasetmentioning

confidence: 99%

Prepended Domain Transformer: Heterogeneous Face Recognition Without Bells and Whistles

George

Mohammadi

Marcel

2023

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…For testing numerous datasets are used. Koley et al [7] invented noise invariant descriptor so-called Gammadion Binary Pattern of Shearlet Coefficients (GBPSC). Initially the noise robust feature maps are derived by using the Shearlet Transform.…”

Section: Related Workmentioning

confidence: 99%

“…In LPQ1, LPQ is implemented by using 5x5 patch and in LPQ2, LPQ is implemented by using 7x7 patch. 1*2 0 0*2 1 0*2 2 1*2 7 1*2 3 0*2 6…”

Section: Noise and Blur Invariant Local Descriptor (Nabild)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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Improved HELBP in Harsh Lightning Variations for Face Recognition

Karanwal

2022

Lecture Notes in Electrical Engineering

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Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor

Cited by 9 publications

References 29 publications

Prepended Domain Transformer: Heterogeneous Face Recognition Without Bells and Whistles

Prepended Domain Transformer: Heterogeneous Face Recognition Without Bells and Whistles

NABILD: Noise And Blur Invariant Local Descriptor for Face Recognition

Improved HELBP in Harsh Lightning Variations for Face Recognition

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