Indian Sign Language gesture classification as single or double handed gestures

The development of a sign language recognition system can have a great impact on the daily lives of humans with hearing disabilities. Recognizing gestures from the Indian Sign Language (ISL) with a camera can be difficult due to complexity of various gestures. The motivation behind the paper is to develop an approach to successfully classify gestures in the ISL under ambiguous conditions from static images. A novel approach involving the decomposition of gestures into single handed or double handed gesture has been presented in this paper. Classifying gesture into these subcategories simplifies the process of gesture recognition in the ISL due to presence of lesser number of gestures in each subcategory. Various approaches making use of Histogram of Gradients (HOG) features and geometric descriptors using KNN and SVM classifiers were tried on a dataset consisting of images of all 26 English alphabets present in the ISL under variable background. HOG features when classified with Support Vector Machine were found to be the most efficient approach resulting in an accuracy of 94.23%.

[1]  M.K. Bhuyan,et al.  A Framework for Hand Gesture Recognition with Applications to Sign Language , 2006, 2006 Annual IEEE India Conference.

[2]  Ming Ouhyoung,et al.  A real-time continuous gesture recognition system for sign language , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[3]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  K. Assaleh,et al.  Arabic sign language recognition in user-independent mode , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[5]  S. Majumder,et al.  Shape, texture and local movement hand gesture features for Indian Sign Language recognition , 2011, 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011).

[6]  Jason Jianjun Gu,et al.  Combining features for Chinese sign language recognition with Kinect , 2014, 11th IEEE International Conference on Control & Automation (ICCA).

[7]  Sudeep D. Thepade,et al.  Recognition of American sign language using LBG vector quantization , 2014, 2014 International Conference on Computer Communication and Informatics.

[8]  Anand Singh Jalal,et al.  Recognition of Indian Sign Language using feature fusion , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[9]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).