Hyperspectral Face Databases for Facial Recognition Research

Cho, Woon; Koschan, Andreas; Abidi, Mongi A.

doi:10.1007/978-3-319-28501-6_3

Cited by 5 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several techniques have been proposed to achieve illumination invariant face recognition, such as a 3D face scanner [10][11][12], hyperspectral imaging (HSI) [13][14][15], thermal imaging (TI) [16][17][18], Kinect sensors [19][20][21][22], and near-infrared (NIR) imaging techniques [23,24]. Experimental results [16][17][18]23,24] have shown that both TI and NIR techniques can achieve illumination invariance to some extent.…”

Section: Introductionmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

View full text Add to dashboard Cite

Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

show abstract

Section: Introductionmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

View full text Add to dashboard Cite

show abstract

“…We would also like to port our Matlab program to C/C++ so that our method can be very fast in hyperspectral face recognition. We might conduct experiments for other hyperspectral face databases as shown in [23].…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral face recognition with log‐polar Fourier features and collaborative representation based voting classifiers

2016

View full text Add to dashboard Cite

Hyperspectral imagery analysis has become a popular topic for improving face recognition accuracy. Nevertheless, it encounters difficulty in data acquisition, low signal‐to‐noise ratio, and high dimensionality. As a result, there exists a need to develop better algorithms in order to achieve higher classification rates. In this study, the authors propose a new method for hyperspectral face recognition with very competitive experimental results. Since there is a significant amount of noise in every spectral band, they reduce noise adaptively from each spectral band by using any image denoising method, e.g. block matching and 3D filtering. They then crop each face according to its eye coordinates so that translation invariance can be achieved. They conduct log‐polar transform to each cropped face image and extract 2D Fourier spectrum from them. In this way, the extracted features are approximately invariant to translation, rotation, and scaling. They use the collaborative representation‐based classifier with voting for hyperspectral face recognition. They perform some experiments to test the authors’ new method for hyperspectral face recognition with very promising results.

show abstract

“…Hyperspectral images were acquired under controlled conditions (Fig 3). Subjects position were stabilized using head rest stop, whereas subjects [38] 480-720nm 25 82 PolyU-HFSD [39] 400-720nm 33 25 Stanford-1 Hyperspectral [40] 400-1000nm 160 45 Stanford-2 Hyperspectral [41] 970-2500nm 256 45 IRIS-HFD-2014 Hyperspectral [41] 420-700nm 29 130 UWA-HSFD Hyperspectral [26] 400-720nm 33 70 maintained still pose and height. Three 600-Watt halogen lamps were used at the angle of -45, 0, and 45 degrees for illumination.…”

Section: A Cmu Hyperspectral Face Databasementioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

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

show abstract

Hyperspectral Face Databases for Facial Recognition Research

Cited by 5 publications

References 20 publications

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

Hyperspectral face recognition with log‐polar Fourier features and collaborative representation based voting classifiers

Current Advances in Hyperspectral Face Recognition

Contact Info

Product

Resources

About