Abstract
Research on automatic sign language recognition (ASLR) has mostly been conducted from a machine learning perspective. We propose to implement results from human sign recognition studies in ASLR. In a previous study it was found that handshape is important for human sign recognition. The current paper describes the implementation of this conclusion: using handshape in ASLR. Handshape information in three different representations is added to an existing ASLR system. The results show that recognition improves, except for one representation. This refutes the idea that extra (handshape) information will always improve recognition. Results also vary per sign: some sign classifiers improve greatly, others are unaffected, and rare cases even show decreased performance. Adapting classifiers to specific sign types could be the key for future ASLR.
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References
Ong, S.C., Ranganath, S.: Automatic sign language analysis: A survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 873–891 (2005)
von Agris, U., Zieren, J., Canzler, U., Bauer, B., Kraiss, K.F.: Recent developments in visual sign language recognition. Univers. Access Inf. Soc. 6(4), 323–362 (2008)
Derpanis, K.G., Wildes, R.P., Tsotsos, J.K.: Hand gesture recognition within a linguistics-based framework. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 282–296. Springer, Heidelberg (2004)
Vogler, C., Metaxas, D.: Handshapes and movements: Multiple-channel american sign language recognition. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 247–258. Springer, Heidelberg (2004)
ten Holt, G.A., van Doorn, A.J., de Ridder, H., Reinders, M.J.T., Hendriks, E.A.: Signs in which handshape and hand orientation are either not visible or are only partially visible: What is the consequence for lexical recognition? Sign Language Studies 10(1) (2009)
ten Holt, G.A., van Doorn, A.J., de Ridder, H., Reinders, M.J., Hendriks, E.A.: Which fragments of a sign enable its recognition? Sign Language Studies 9(2), 211–239 (2009)
ten Holt, G.A., Arendsen, J., de Ridder, H., van Doorn, A.J., Reinders, M.J., Hendriks, E.A.: Sign language perception research for improving automatic sign and gesture recognition. In: SPIE Human Vision and Electronic Imaging XIV, vol. 7240. SPIE, Bellingham (2009)
Lichtenauer, J.F., Hendriks, E.A., Reinders, M.J.: Sign language recognition by combining statistical dtw and independent classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 2040–2046 (2008)
Lichtenauer, J.F., ten Holt, G.A., Reinders, M.J.T., Hendriks, E.A.: Person-independent 3d sign language recognition. In: Sales Dias, M., Gibet, S., Wanderley, M.M., Bastos, R. (eds.) GW 2007. LNCS (LNAI), vol. 5085, pp. 69–80. Springer, Heidelberg (2009)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)
Caridakis, G., Diamanti, O., Karpouzis, K., Maragos, P.: Automatix sign language recognition: vision based feature extraction and probabilistic recognition scheme from multiple cues. In: Proceedings of ACM PETRA (2008)
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ten Holt, G.A., Reinders, M.J.T., Hendriks, E.A., de Ridder, H., van Doorn, A.J. (2010). Influence of Handshape Information on Automatic Sign Language Recognition. In: Kopp, S., Wachsmuth, I. (eds) Gesture in Embodied Communication and Human-Computer Interaction. GW 2009. Lecture Notes in Computer Science(), vol 5934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12553-9_27
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DOI: https://doi.org/10.1007/978-3-642-12553-9_27
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