Hand Localization and Fingers Features Extraction: Application to Digit Recognition in Sign Language
- 1.6k Downloads
We present in this paper an approach of hand gesture analysis that aims at recognizing a digit. The analysis is based on extracting a set of features from a hand image and then combining them by using an induction graph. The most important features we extract from each image are the fingers locations, their heights and the distance between each pair of fingers. Our approach consists of three steps: (i) Hand localization, (ii) fingers extraction and (iii) features identification and combination to digit recognition. Each input image is assumed to contain only one hand with black background, thus we apply a classifier based on one skin color to identify the skin pixels. In the finger extraction step, we attempt to remove all the hand components except the fingers, this process is based on the hand anatomy properties. The final step is based on histogram representation of the detected fingers which results in the features identification, which results in the digit recognition. The approach is invariant to scale, rotation and translation of the hand. Some experiments have been undertaken to show the effectivness of the proposed approach.
KeywordsSign language keyboard application digits recognition
Unable to display preview. Download preview PDF.
- 2.Bellik, Y.: Modality Integration: Speech and Gesture. Survey of the State of the Art in Human Language Technology, Section 9.4, 307–309 (1996)Google Scholar
- 3.Braffort, A.: Reconnaissance et Compréhension de gestes, application à la langue des signes, thèse de l’université de Paris XI, spécialité informatique (1996)Google Scholar
- 4.Iwai, Y., Yagi, Y., Yachida, M.: Gesture Recognition using Colored Gloves. In: Proc. of ICPR, pp. 662–666 (1996)Google Scholar
- 5.Berard, F., Coutaz, J., Crowley, J.L.: Finger Tracking as Input Device for Augmented Reality. In: Proc. Intel Workshop on Automatic Face and Gesture-Recognition, Zurich, Switzerland (1995)Google Scholar
- 7.Martin, J., Crowley, J.L.: An Appearance-Based Approach to Gesture-Recognition. In: Proc. of 9th Conf. on Image Analysis and Processing, Italy (1997)Google Scholar
- 10.Chai, D., Bouzerdoum, A.: A Bayesian Approach to Skin Color Classification in YCbCr Color Space. In: IEEE Region Ten Conference TENCON, vol. 2, pp. 421–424 (2000)Google Scholar
- 12.Quinlan, J.R.: Induction of Decision Trees. Machine Learning, 81–106 (2003)Google Scholar
- 13.Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 14.Zighed, D.A., Rakotomalala, R.: Graphes d’Induction - Apprentissage et Data Mining. Hermes (2000)Google Scholar
- 15.Rakotomalala, R., Lallich, S.: Handling noise with generalized entropy of type beta in induction graphs algorithm. In: Int. Conf. on Computer Science and Informatics, pp. 25–27 (1998)Google Scholar