Local Zernike Moments: A new representation for face recognition

Sarıyanidi, Evangelos; Dagli, Volkan; Tek, Salih Cihan; Tunç, Birkan; Gokmen, M.

doi:10.1109/icip.2012.6466927

Cited by 30 publications

(49 citation statements)

References 8 publications

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“…In [12], Quantised Local Zernike Moment (QLZM) is used to describe the neighbourhood of a face sub-region. The Local Zernike moments have more discriminant power than other image features, e.g., local phase-magnitude histogram(H-LZM), cascaded LZM transformation (H-LZM 2 ) and local binary pattern (LBP) [13].…”

Section: Introductionmentioning

confidence: 99%

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatiotemporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy. To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state-of-arts methods, the proposed framework demonstrates a superior performance.

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Section: Introductionmentioning

confidence: 99%

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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“…In problems such as face recognition, rather than the holistic characterization of images, local statistics have more importance [14]. Therefore, the local Zernike moments (LZM) [39] transformation was proposed to extract local variations by calculating these moments around each pixel on the face images.…”

Section: Local Zernike Momentsmentioning

confidence: 99%

“…To evaluate the method, we used three different datasets with different properties. The contributions of this study are as follows: (1) In previous studies using LZM transformation, methods have been proposed for only face identification [39,42,43,53] or verification [41]. In this study, we develop an unsupervised face recognition scheme for both identification and verification; (2) In most of the LZM-based studies [39,41,42,53], only one dataset is used to evaluate the methods.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this study are as follows: (1) In previous studies using LZM transformation, methods have been proposed for only face identification [39,42,43,53] or verification [41]. In this study, we develop an unsupervised face recognition scheme for both identification and verification; (2) In most of the LZM-based studies [39,41,42,53], only one dataset is used to evaluate the methods. In this study, using Face Recognition Technology (FERET) [54], Labeled Faces in the Wild (LFW) [55], and Surveillance Cameras Face (SCface) [56] datasets, which have very different characteristics from each other, we show the performance of MS-LZM (the code is available at https://github.com/ebsrn/lzm-face) on unconstrained and low-resolution images acquired in uncontrolled environments or in infrared spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to [39], in MS-LZM, the LZM transformation is applied twice in succession. With the first transformation, the effects of lighting and blurring on the images are removed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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Abstract:In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

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2D and 3D Orthogonal Moments

2016

2D and 3D Image Analysis by Moments

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Local Zernike Moments: A new representation for face recognition

Cited by 30 publications

References 8 publications

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2D and 3D Orthogonal Moments

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