Face recognition using Zernike and complex Zernike moment features

Singh, Chandan; Mittal, Neerja; Walia, Ekta

doi:10.1134/s1054661811010044

Cited by 56 publications

(15 citation statements)

References 19 publications

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“…ZMs employ complex Zernike polynomials as its moment basis set [22], and have been used to recognise facial expressions [23]. The rotation invariance of Zernike-based facial features is discussed in [9,10]. QLZM is used in [12] for recognising facial expressions.…”

Section: Related Workmentioning

confidence: 99%

“…However, current state-of-the-art methods only focus on a small subset of possible shape representation, e.g., point-based methods that represent a face using the locations of several discrete points. Noting that image moments can describe simple properties of a shape, e.g., its area (or total intensity), its centre and its orientation, Zernike moments (ZMs) have been used to represent a face and facial expressions in [9,10]. Zernike moments are rotation invariant features, which can be used to address in-plane head pose variation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Related Workmentioning

confidence: 99%

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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“…7-11. Comparisons has been made with SSIM, FSIM and the Zernike moments method (Singh et al, 2011). The scores of similarity provided by the proposed metrics are more robust and give more confidence.…”

Section: Simulation Resultsmentioning

confidence: 99%

Schoenberg Logarithmic Image Similarity in Prewitt-Gabor-Zernike Domain

Ali¹,

Hussain²,

Mechee³

et al. 2017

American Journal of Applied Sciences

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This paper represents a new approach for face recognition that incorporates Prewitt edge detection, Gabor filter and Zernike moments to transform the image into a unified domain. On this joint domain, five distance metrics are constructed using Schoenberg transform for the purpose of defining efficient similarity measures for holistic face recognition. The proposed Schoenberg similarity applies Schoenberg transform to the logarithm of five existing distance metrics: Minkowski, City-Block, Euclidean, Soergel and Lorentzian metrics. The constructed Schoenberg logarithmic metrics are called SL-Minkowski, SL-City-Block, SL-Euclidean, SL-Soergel and SL-Lorentzian. These distance metrics are utilized as similarity measures after being normalized over the range [0,1] for fair comparison with existing measures. The proposed Schoenberg system can resist three problems: Change in illumination, pose and facial expression. Simulation results show that the proposed distance measures have superior performance as compared to the classical metrics: Structural Similarity Index Measure (SSIM) and Feature-based Similarity Measure (FSIM). Performance criteria are the recognition rate and the recognition confidence, defined as the similarity difference between the best match and the second-best match in the face database.

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“…The calculation of Zernike moments has been well described in many monographs and articles (Babkina et al, 2013;Singh, Mittal, & Walia, 2011;Singh, Walia, & Upneja, 2013). In order to let readers have a better understanding of our method, a brief description is given as follows: Note: Concentration unit is mg L À1 .…”

Section: Calculation Of Zernike Momentsmentioning

confidence: 99%