Abstract
Local feature descriptors are widely employed to describe local properties of image patches when constructing a discriminative visual representation for efficient classification. Image gradient is a robust characteristic of digital image which possesses localized spatial information for each pixel along all the directions. Utilizing gradient information at the pixel level is a commonly used technique in various fields of digital image processing, especially while designing Local Feature Descriptor (LFD). In this paper, the magnitude and direction of image gradient is utilized to design an LFD. We propose a novel LFD based on Complex Gradient Function (CGF), which effectively maps a pixel from the spatial plane into its complex extension accommodating the magnitude and direction of image gradient at that pixel. To justify the genericness of the proposed descriptor, we have exploited it on two different kinds of applications, namely, human action recognition (HAR) from depth sequences and human authentication using iris biometrics. Robustness and efficiency of our descriptor are demonstrated with extensive experimental analysis on benchmark datasets IITDelhi, MMU-v2, CASIA-Iris, UBIRIS, and MICHE I for iris authentication, and MSR Action 3D dataset and MSR Gesture 3D dataset for human action recognition.
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References
Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (surf). Comput Vis Image Underst. 2008;110(3):346–59.
Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W. Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell. 2010;32(9):1705–20.
Chen C, Liu K, Kehtarnavaz N. Real-time human action recognition based on depth motion maps. J Real-time Image Process. 2016;12(1):155–63.
Chen C, Jafari R, Kehtarnavaz N. Action recognition from depth sequences using depth motion maps-based local binary patterns. In: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on. IEEE; 2015. p. 1092–1099.
Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: International conference on computer vision & pattern recognition (CVPR’05), vol. 1. IEEE Computer Society; 2005. p. 886–893.
De Marsico M, Nappi M, Narducci F, Proença H. Insights into the results of miche i-mobile iris challenge evaluation. Pattern Recogn. 2018;74:286–304.
Deng W, Hu J, Guo J. Compressive binary patterns: designing a robust binary face descriptor with random-field eigenfilters. IEEE Trans Pattern Anal Mach Intell. 2019;41(3):758–67.
Du Y, Wang W, Wang L. Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 1110–1118.
Guo Y, Bennamoun M, Sohel F, Lu M, Wan J, Kwok NM. A comprehensive performance evaluation of 3d local feature descriptors. Int J Comput Vis. 2016;116(1):66–89.
Institute of Automation, Chinese Academy of Sciences. CASIA Iris Database. http://biometrics.idealtest.org/
Ke Y, Sukthankar R, et al. Pca-sift: a more distinctive representation for local image descriptors. CVPR. 2004;2(4):506–13.
Kumar A, Passi A. Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn. 2010;43(3):1016–26.
Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems. In: 2014 fourth international conference on advanced computing communication technologies. 2014. p. 5–12. 10.1109/ACCT.2014.74
Kurakin A, Zhang Z, Liu Z. A real time system for dynamic hand gesture recognition with a depth sensor. In: 2012 Proceedings of the 20th European signal processing conference (EUSIPCO). IEEE; 2012. p. 1975–1979.
Li W, Zhang Z, Liu Z. Action recognition based on a bag of 3d points. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops. IEEE; 2010. p. 9–14.
Liu Z, Zhang C, Tian Y. 3d-based deep convolutional neural network for action recognition with depth sequences. Image Vis Comput. 2016;55:93–100.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.
Malaysia Multimedia University Iris Database. http://pesona.mmu.edu
Mian AS, Bennamoun M, Owens R. Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans Pattern Anal Mach Intell. 2006;28(10):1584–601.
Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell. 2005;27(10):1615–30. https://doi.org/10.1109/TPAMI.2005.188.
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):971–87.
Oreifej O, Liu Z. Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2013. p. 716–723.
Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA. The ubirisv2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell. 2010;32(8):1529–35.
Rahmani H, Mahmood A, Huynh DQ, Mian A. Real time action recognition using histograms of depth gradients and random decision forests. In: IEEE winter conference on applications of computer vision. IEEE; 2014. p. 626–633.
Roy SK, Chanda B, Chaudhuri BB, Banerjee S, Ghosh DK, Dubey SR. Local directional zigzag pattern: a rotation invariant descriptor for texture classification. Pattern Recogn Lett. 2018;108:23–30.
Shekar BH, Rathnakara Shetty P, Sharmila Kumari M, Mestetsky L. Action recognition using undecimated dual tree complex wavelet transform from depth motion maps/depth sequences. In: ISPRS-international archives of the photogrammetry, remote sensing and spatial information sciences, XLII-2/W12. 2019. p. 203–209. 10.5194/isprs-archives-XLII-2-W12-203-2019
Shekar B, Bhat SS, Mestetsky L. Iris recognition by learning fragile bits on multi-patches using monogenic riesz signals. In: International conference on pattern recognition and machine intelligence. Springer; 2019. p. 462–471.
Shetty PR, Shekar B, Mestetsky L, Prasad MM. Stacked filter bank based descriptor for human action recognition from depth sequences. In: 2019 IEEE conference on information and communication technology. IEEE; 2019. p. 1–6.
Vieira AW, Nascimento ER, Oliveira GL, Liu Z, Campos MFM. Stop: space-time occupancy patterns for 3d action recognition from depth map sequences. In: Alvarez L, Mejail M, Gomez L, Jacobo J, editors. Progress in pattern recognition, image analysis, computer vision, and applications. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 252–9.
Vieira AW, Nascimento ER, Oliveira GL, Liu Z, Campos MF. On the improvement of human action recognition from depth map sequences using space-time occupancy patterns. Pattern Recogn Lett. 2014;36:221–7.
Wang J, Liu Z, Chorowski J, Chen Z, Wu Y. Robust 3d action recognition with random occupancy patterns. In: Computer vision–ECCV 2012. Springer; 2012. p. 872–885.
Xia L, Chen CC, Aggarwal JK. View invariant human action recognition using histograms of 3d joints. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops. IEEE; 2012. p. 20–27.
Xia Z, Yuan C, Lv R, Sun X, Xiong NN, Shi YQ. A novel weber local binary descriptor for fingerprint liveness detection. IEEE Trans Syst Man Cybern Syst. 2018
Yang X, Tian YL. Eigenjoints-based action recognition using naïve-bayes-nearest-neighbor. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops. 2012. p. 14–19. 10.1109/CVPRW.2012.6239232
Yang X, Zhang C, Tian Y. Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM international conference on multimedia. ACM; 2012. p. 1057–1060.
Zhang C, Tian Y. Edge enhanced depth motion map for dynamic hand gesture recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2013. pp. 500–505.
Zhao C, Chen M, Zhao J, Wang Q, Shen Y. 3d behavior recognition based on multi-modal deep space-time learning. Appl Sci. 2019;9(4):716.
Zuo Z, Wei B, Chao F, Qu Y, Peng Y, Yang L. Enhanced gradient-based local feature descriptors by saliency map for egocentric action recognition. Appl Syst Innov. 2019;2(1):7.
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This work is supported jointly by the Department of Science & Technology, Govt. of India and Russian Foundation for Basic Research, Russian Federation under Grant No. INT/RUS/RFBR/P-248.
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Shekar, B.H., Shetty, P.R., Bhat, S.S. et al. Complex Gradient Function Based Image Descriptor. SN COMPUT. SCI. 4, 42 (2023). https://doi.org/10.1007/s42979-022-01436-y
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DOI: https://doi.org/10.1007/s42979-022-01436-y