Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition

Kaur, Bineet; Joshi, Garima

doi:10.1155/2016/6727806

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…Exploiting the benefits of AI algorithm, we proposed to build features from PCC [23] into optimized AI algorithm. PCCbased feature selection minimized features dimensionality [24] and AI learning complexity. This section describe architectures of the following adopted AI algorithms; (a) ANFIS-SC (b) SVM-ECOC and steps of the methods are described in flowchart 1.…”

Section: Methodsmentioning

confidence: 99%

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

et al. 2022

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Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) have been proposed to recognize distinct and discriminant features of biometric information (intrinsic hand features and demographic cues) with good classification accuracy. Unfortunately, due to nonlinearity in distinct and discriminant features of biometric information, accuracy of SVM and ANFIS is reduced. As a result, optimized AI algorithms ((ANFIS) with subtractive clustering (ANFIS-SC) and SVM with error correction output code (SVM-ECOC)) have shown to be effective for biometric information recognition. In this paper, we compare the performance of the ANFIS-SC and SVM-ECOC algorithms in their effectiveness at learning essential characteristics of intrinsic hand features and demographic cues based on Pearson correlation coefficient (PCC) feature selection. Furthermore, the accuracy of these algorithms are presented, and their recognition performances are evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), scatter index (SI), mean absolute deviation (MAD), coefficient of determination (R 2 ), Akaike's Information Criterion (AICc) and Nash-Sutcliffe model efficiency index (NSE). Evaluation results show that both SVM-ECOC and ANFIS-SC algorithms are suitable for accurately recognizing soft biometric information on basis of intrinsic hand measurements and demographic cues. Moreover, comparison results demonstrated that ANFIS-SC algorithms can provide better recognition accuracy, with RMSE, AICc, MAPE, R 2 and NSE values of ≤ 3.85, 2.39E+02, 0.18%, ≥ 0.99 and ≥ 99, respectively.

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Section: Methodsmentioning

confidence: 99%

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

et al. 2022

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“…Basically, there are two major approaches for deriving moment invariants: (1) the indirect method that is founded on the algebraic relation between the image moments and the geometric ones, in order to express moment invariants in terms of geometric moment invariants [34] and (2) the explicit method that seeks to directly derive invariants from the orthogonal moments [34]. In this connection, there exist a wide number of methods that have been proposed for hand gesture recognition application based on image moment features, like Hu, Tchebichef, Krawtchouk and Zernike moments [35][36][37][38][39][40][41]. However, the capabilities of the moment invariants in hand gesture recognition by using RGB-D images have not been sufficiently explored, and only very few papers have appeared with this concern.…”

Section: Dynamic (Video)mentioning

confidence: 99%

Robust hand gesture recognition system based on a new set of quaternion Tchebichef moment invariants

Elouariachi

Benouini

Zenkouar

et al. 2020

Pattern Anal Applic

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Hand gesture recognition is a challenging task due to the complexity of hand movements and to the variety among the same gesture performed by distinct subjects. Recent technologies, such as Kinect sensor, provide new opportunities, allowing to capture both RGB and depth (RGB-D) images, which offer high discriminant information for efficient hand gesture recognition. In the aspect of feature extraction, the traditional methods process the RGB and depth information independently. In this paper, we propose a robust hand gesture recognition system based on a new feature extraction method, fusing RGB images and depth information simultaneously, by using the quaternion algebra that provide a more robust and holistical representation. In fact, we introduce, for the first time, a novel type of feature extraction method, named quaternion Tchebichef moment invariants. The novelty of the proposed method in this paper lies in the direct derivation of invariants from their orthogonal moments, based on the algebraic properties of the discrete Tchebichef polynomials. The proposed approach based on quaternion algebra is suggested to process the four components holistically, for a robust and efficient hand gesture recognition system. The obtained experimental and theoretical results demonstrate that the present approach is very effective for addressing the problem of hand gesture recognition and have proved its robustness against geometrical distortion, noisy conditions and complex background compared to the state of the art, indicating that it could be highly useful for many computer vision applications. Keywords Hand gesture recognition • Tchebichef moments • Moment invariants • Quaternion algebra • Complex background • RST invariants

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“…In this work, histogram statistical properties of reorganized block-based Krawtchouk moments are exploited to design this feature set. This approach is developed based on the fact that low order Krawtchouk moments often have better image reconstruction performance than Tchebichef moments [ 21 , 22 ] do, indicating as a consequence that most of the image energy is compacted into these low order moments. As a result, it is reasonable to extend the strategy proposed in [ 19 ] to Krawtchouk moments with some improvements taking advantage of the specificities of Krawtchouk moments.…”

Section: Proposed Blind Image Forensicsmentioning

confidence: 99%

“…It is known that Krawtchouk moments have better image reconstruction performance than Tchebichef moments [ 20 , 21 ] and have already find applications in image recognition [ 22 ] and fractional transform domain construction [ 23 ]. Therefore, in this work, we propose to take advantage of them so as to build a new feature set in which the features are extracted from the histogram statistical properties of reorganized block-based Krawtchouk moments (HRBK).…”

Section: Introductionmentioning

confidence: 99%

Medical Image Blind Integrity Verification with Krawtchouk Moments

Zhang

Liu

Chen

et al. 2018

International Journal of Biomedical Imaging

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A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images—solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set.

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Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition

Cited by 16 publications

References 30 publications

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

Robust hand gesture recognition system based on a new set of quaternion Tchebichef moment invariants

Medical Image Blind Integrity Verification with Krawtchouk Moments

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