Hyperspectral face recognition via feature extraction and CRC‐based classifier

Chen, Guangyi; Li, Changjun; Sun, Wei

doi:10.1049/iet-ipr.2016.0722

“…In the following step each cluster is then decomposed to a coefficient tensor and three dictionary matrices. HSI can now [26] 2015 UWA 33 400 -720 nm 98% Chen [64] 2016 PolyU 33 400 -720nm 95%…”

Section: Hyperspectral Face Recognition Techniquesmentioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

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Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

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“…On the way to achieve better results Chen et al came up with [55]. First of all, adaptive denoising of each spectral band was carried out.…”

Section: Hyperspectral Face Recognition Techniquesmentioning

confidence: 99%

“…In the following step each cluster is then decomposed to a coefficient tensor and three dictionary matrices. HSI can now [26] 2015 UWA 33 400 -720 nm 98% Chen [64] 2016 PolyU 33 400 -720nm 95% be reconstructed by the multiplication of coefficient tensor and dictionary matrices. This leads to the removal of redundancy.…”

Section: Hyperspectral Face Recognition Techniquesmentioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

3

0

View full text Add to dashboard Cite

Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

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“…Contrary to palmprint biometrics, hand vein biometrics, such as dorsal hand veins [10][11][12] and palm veins [13,14], are studied in security with advantages in liveness detection and anti-spoofing [12]. In the meantime, hyperspectral technology known from remote sensing [15][16][17][18], has been introduced in biometrics, where it is applied in high-security scenarios [18]. Therefore, hand biometrics when combined with hyperspectral technology, is a potential solution achieving better personal authentication [19,20].…”

Section: Introductionmentioning

confidence: 99%

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Nie¹,

Zhang²,

Zhao³

2019

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Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.

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Hyperspectral face recognition via feature extraction and CRC‐based classifier

Cited by 28 publications

References 15 publications

Current Advances in Hyperspectral Face Recognition

Current Advances in Hyperspectral Face Recognition

Current Advances in Hyperspectral Face Recognition

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

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