Face Recognition from Visible and Near-Infrared Images Using Boosted Directional Binary Code

Shen, Linlin; He, Jinwen; Wu, Shipei; Zheng, Songhao

doi:10.1007/978-3-642-25944-9_52

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…In this section, five standard databases, including FGNET Aging database (Lanitis, Taylor, and Cootes 2002), Visible (VI) and Near-Infrared (NIR) Face Database (Shen et al 2011), OSR database (Parikh and Grauman 2011), PubFig database (Kumar et al 2009) and LFW database (Huang et al 2007) (Davis et al 2007), LDML (Guillaumin, Verbeek, and Schmid 2009), IDENTITY (Huo, Nie, and Huang 2016) and MAHAL (Huo, Nie, and Huang 2016). For our method, we set λ = 0.001, ξ = 10 −5 and R = 5.…”

Section: Methodsmentioning

confidence: 99%

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Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning

Luo

Huang

2018

AAAI

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Mahalanobis Metric Learning (MML) has been actively studied recently in machine learning community. Most of existing MML methods aim to learn a powerful Mahalanobis distance for computing similarity of two objects. More recently, multiple methods use matrix norm regularizers to constrain the learned distance matrixMto improve the performance. However, in real applications, the structure of the distance matrix M is complicated and cannot be characterized well by the simple matrix norm. In this paper, we propose a novel robust metric learning method with learning the structure of the distance matrix in a new and natural way. We partition M into blocks and consider each block as a random matrix variate, which is fitted by matrix variate Gaussian mixture distribution. Different from existing methods, our model has no any assumption on M and automatically learns the structure of M from the real data, where the distance matrix M often is neither sparse nor low-rank. We design an effective algorithm to optimize the proposed model and establish the corresponding theoretical guarantee. We conduct extensive evaluations on the real-world data. Experimental results show our method consistently outperforms the related state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

“…An experiment is performed on the Visible (VI) and Near-Infrared (NIR) Face Database (Shen et al 2011). The NIR and VI database is collected using developed dual camera system from 215 subjects.…”

Section: Experiments On the VI And Nir Face Databasementioning

confidence: 99%

Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning

Luo

Huang

2018

AAAI

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“…Both gallery and probe sets are composed of only NIR images [25,26,27,28,29,30,31]. (ii) Image Synthesis: usage of image synthesis to convert NIR images to case that is more realistic in a law enforcement scenario).…”

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confidence: 99%

On designing practical long range near infrared-based face recognition systems

Bourlai

Mavridis

Narang

2016

Image and Vision Computing

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Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching

Maeng

Liao

Kang

et al. 2013

Lecture Notes in Computer Science

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Abstract. Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.

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Face Recognition from Visible and Near-Infrared Images Using Boosted Directional Binary Code

Cited by 6 publications

References 13 publications

Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning

Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning

On designing practical long range near infrared-based face recognition systems

Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching

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