A static hand gesture recognition algorithm using k-mean based radial basis function neural network

The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.

[1]  Jochen Triesch,et al.  A System for Person-Independent Hand Posture Recognition against Complex Backgrounds , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Lalit Gupta,et al.  Gesture-based interaction and communication: automated classification of hand gesture contours , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  S Padam Priyal,et al.  A study on static hand gesture recognition using moments , 2010, 2010 International Conference on Signal Processing and Communications (SPCOM).

[5]  David Zeltzer,et al.  A survey of glove-based input , 1994, IEEE Computer Graphics and Applications.

[6]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Collin Wang,et al.  A virtual end-effector pointing system in point-and-direct robotics for inspection of surface flaws using a neural network based skeleton transform , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[8]  Surendra Ranganath,et al.  Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Simon Haykin,et al.  Neural networks , 1994 .

[10]  K. Wan,et al.  Dynamic gesture recognition based on the probabilistic distribution of arm trajectory , 2008, 2008 IEEE International Conference on Mechatronics and Automation.

[11]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[12]  Yuntao Cui,et al.  A Learning-Based Prediction-and-Verification Segmentation Scheme for Hand Sign Image Sequence , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Goutam Saha,et al.  In search of an optimization technique for Artificial Neural Network to classify abnormal heart sounds , 2009, Appl. Soft Comput..

[14]  Prashan Premaratne,et al.  Consumer electronics control system based on hand gesture moment invariants , 2007 .

[15]  Lalit Gupta,et al.  Robust automatic target recognition using a localized boundary representation , 1995, Pattern Recognit..

[16]  Tosiyasu L. Kunii,et al.  Model-based analysis of hand posture , 1995, IEEE Computer Graphics and Applications.

[17]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Ruiduo Yang,et al.  Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Vladimir Pavlovic,et al.  Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..