Hand posture recognizer based on separator wavelet networks

Tahani Bouchrika; Olfa Jemai; Mourad Zaied; Chokri Ben Amar

doi:10.1117/12.2228425

8 December 2015 Hand posture recognizer based on separator wavelet networks

Tahani Bouchrika, Olfa Jemai, Mourad Zaied, Chokri Ben Amar

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98750F (2015) https://doi.org/10.1117/12.2228425
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.

Citation Download Citation

Tahani Bouchrika, Olfa Jemai, Mourad Zaied, and Chokri Ben Amar "Hand posture recognizer based on separator wavelet networks", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750F (8 December 2015); https://doi.org/10.1117/12.2228425

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