Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A survey of skin-color modeling and detection methods
Pattern Recognition
Finger identification and hand posture recognition for human-robot interaction
Image and Vision Computing
Biometric verification/identification based on hands natural layout
Image and Vision Computing
A Multistage Hierarchical Algorithm for Hand Shape Recognition
IMVIP '09 Proceedings of the 2009 13th International Machine Vision and Image Processing Conference
ACHI '10 Proceedings of the 2010 Third International Conference on Advances in Computer-Human Interactions
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Person recognition by hand shape based on skeleton of hand image
Pattern Recognition and Image Analysis
Influence of handshape information on automatic sign language recognition
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Exploiting phonological constraints for handshape inference in ASL video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
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This study proposes compact hand extraction to assist in computerized handshape recognition. First, we devised an image enhancement technique based on singular value decomposition to remove dark backgrounds by reserving the skin color pixels of a hand image. Then, the polynomial approximation YC"bC"r color model was used to extract the hand. After alignment, we applied lighting compensation to the adaptable singular value decomposition. Finally, a hierarchical pyramid sampling algorithm was used to reduce the impact of variations in handshape. We also constructed a self-eigenhand recognizer with genetic algorithms (GA) for selecting discriminant eigenvector subsets for classification. Although our approach maximizes the differences in hand images for various handshapes, it also minimizes variations in lighting and pose for the same handshape. Experimental results for images from our database and a live sequence showed that our method functioned more efficiently than conventional ones that do not use compact hand extraction against complex scenes. For the 768 images included in inside testing, our classification system achieved an AAR of 99.55% and an FAR of 0.0001%. For live testing, the classification system achieved an accuracy rate of 91.7%, with an error rate of 8.3%. Regarding speed, our system was faster than conventional ones. Our images size was 160x120 pixels, operating at an average processing time of less than 1s per handshape (using an AMD64 Athlon CPU 2.0GHz personal computer).