Near infrared face recognition using Zernike moments and Hermite kernels

Farokhi, Sajad; Sheikh, Usman Ullah; Flusser, Jan; Yang, Bo

doi:10.1016/j.ins.2015.04.030

Cited by 58 publications

(20 citation statements)

References 56 publications

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“…With respect to the identification performance of traditional algorithms, the methods fusing global and local features (95.64% for the ZMUDWT and 100% for the ZMHK) outperform LBP (89.76% and 87.34%), and the ZMHK outperforms the ZMUDWT. This result is in accordance with that in [28]. This approach to selecting the training set and developing the test sets is designed to simulate a more realistic surveillance application (in which the face is expected to be recognised when an object may be in motion or be obscured by image noise) by using a limited training set (three pictures of the normal face for each person in this research).…”

Section: Experimental Results Using Normal Facessupporting

confidence: 72%

“…The γ and σ in the HK were set to 13 and two, respectively. The image was divided into eight blocks, according to [28], to extract features. The feature fusion and classification methods in [28] were used.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we will test NIRFaceNet, LBP + PCA [23], LBP Histogram [47], ZMUDWT [27], ZMHK [28], and GoogLeNet on the CASIA NIR database [23]. Facial expression, head pose variation, salt-and-pepper and Gaussian noise, motion and Gaussian blur are added to the dataset to compare the robustness of the algorithms.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Using the same CASIA NIR database, Sajad et al [26] later tested global feature extraction methods (ZM, independent component analysis, radon transform plus discrete cosine transform, radon transform plus discrete wavelet transform) and local feature extraction methods (LBP, Gabor wavelets, discrete wavelet transform, undecimated discrete wavelet transform), and found ZM and undecimated discrete wavelet transform (UDWT) can achieve the highest recognition rate among global and local feature extraction methods, respectively. To obtain better recognition performance, Sajad et al [27,28] moved on to fuse global and local features and proposed Zernike moment undecimated discrete wavelet transform (ZMUDWT) method and the Zernike moments plus hermite kernels (ZMHK) method as the feature extraction methods for NIR face recognition.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2016

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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.

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Section: Experimental Results Using Normal Facessupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…For validating the idea of NIR FER, Zhao et al [11] collected an NIR facial expression database, called Oulu-CASIA NIR facial expression database, and utilized improved LBP-TOP (Local binary patterns from three orthogonal planes) capturing the dynamic local information from the NIR video sequences. Farokhi, S. et al [12] proposed a NIR face recognition method based on Zernike moments (ZMs) and Hermite kernels (HKs). Gejji, R.S.…”

Section: Introductionmentioning

confidence: 99%

NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

Chen

et al. 2017

Applied Sciences

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Facial expression recognition (FER) under active near-infrared (NIR) illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D), Local binary patterns from three orthogonal planes (LBP-TOP), deep temporal appearance-geometry network (DTAGN), and adapt 3D Convolutional Neural Networks (3D CNN DAP).

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2D and 3D Orthogonal Moments

2016

2D and 3D Image Analysis by Moments

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Near infrared face recognition using Zernike moments and Hermite kernels

Cited by 58 publications

References 56 publications

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

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

NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

2D and 3D Orthogonal Moments

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