Abstract
This paper discusses a Zernike Moment (ZM) based feature vector that can characterize the alphabets of Indian Sign Language (ISL). Sign Language Recognition (SLR) is a multiclass shape classification problem. Studies of human visual system reveal that while observing any scene, the focus is more on the center part and it decreases toward the edges. This became the basis of calculating the ZMs on a unit circular disk. Continuous orthogonal moments such as magnitude of ZM are well known for their shape representing capabilities. However, for a SLR system the highest order of moments is to be estimated, because with increase in order of moments, the feature vector size increases significantly. In order to find the maximum order that is sufficient to classify the shapes of hand silhouettes, performance of various classifiers is analyzed. Results show that increasing the order of ZM, beyond a certain order does not contribute to the improvement in recognition capability. The results improve when the ZM is combined with some basic geometric features and commonly used shape descriptors such as Hu moments (HMs).
This is a preview of subscription content, log in via an institution.
References
Hasan, H. and Kareem, S. A., Static Hand Gesture Recognition using Neural Net-works, Artificial Intelligence Review, 37, 1–35 (2012).
Zhang, Lu, G., Review of Shape Representation and Description Techniques, Journal of Pattern Recognition Society, 37, 1–90 (2004).
Kar, P. and Raina, A. M, Semantic Structure of the Indian Sign Language, International Conference on South Asian Languages, 1–23 (2008).
Ibraheem, P. A. and Khan, R. Z., Survey on Various Gesture Recognition Technologies and Techniques, International Journal of Computer Applications 50(7), 38–44 (2012).
Shu, H., Luo, L. and Coatrieux, J. L., Moment-based Approaches in Image. Part 1: Basic Features, IEEE Engineering in Medicine and Biology Magazine, 25, 70–74 (2007).
Potocnik, B., Assessment of Region-based Moment Invariants for Object Recognition., IEEE International Symposium on Multimedia Signal Processing and Communications, 27–32 (2006).
Nallasivan, G., Janakiraman, S. and Ishwarya, Comparative Analysis of Zernike Moments With Region Grows Algorithm on MRI Scan Images for Brain Tumor Detection, Australian Journal of Basic and Applied Sciences, 9, 1–7 (2015).
Kim, H. S. and Lee, H. K. Invariant image watermarking using Zernike moments, IEEE Trans. Image Process, 13, 766–775, (2003).
Sabhara, R. K., Lee, C .P. and Lim, K. M., Comparative Study of Hu Moments and Zernike Moments in Object Recognition, Smart Computing Review, 3, 166–173 (2013).
Priya, S. P. and Bora, P. K., A Study on Static Hand Gesture Recognition using Moments IEEE International Conference on Signal Processing and Communication), 1–5 (2013).
Khurana, G., Joshi, G. and Kaur, J., Static Hand Gestures Recognition System using Shape Based Features, Recent Advances in Engineering and Computational Sciences, 1–4 (2014).
Mingqiang, Y., Kidiyo, K. and Joseph, R., A Survey of Shape Feature Extraction Techniques, Pattern Recognition Techniques Technology and Applications, 25, 43–90 (2008).
Singh, C., Walia, E. and Upneja, R., Accurate Calculation of Zernike moments, Information Sciences, 233, 255–275 (2013).
Khalid, M. and Hosny, A Systematic Method for Fast Computation of Accurate Full and Subsets Zernike Moments, Information Sciences, 180, 2299–2313 (2010).
Kotsiantis, S, Zaharakis, I. and Pintelas, P., Supervised Machine Learning: A Review of Classification and Combining Techniques, Artificial Intelligence Review, 26, 159–190 (2006).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Joshi, G., Vig, R., Singh, S. (2018). Analysis of Zernike Moment-Based Features for Sign Language Recognition. In: Singh, R., Choudhury, S., Gehlot, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_140
Download citation
DOI: https://doi.org/10.1007/978-981-10-5903-2_140
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5902-5
Online ISBN: 978-981-10-5903-2
eBook Packages: EngineeringEngineering (R0)