Comparison of range check classifier and hybrid network classifier for hand sign recognition system
Hiroomi Hikawa, Seito Yamazaki, Tatsuya Ando, Seiji Miyoshi, Yutaka Maeda · 2010
This paper discusses two types of vector classifiers for the hand posture recognition system. One classifier is based on the range check (RC) function, and the other is based on the hybrid network that is made of self-organizing map (SOM) and Hebbian network. In case the learning data and the testing data are different, the system with the hybrid network classifier outperforms the other system in the recognition rate by 9%. Two types of implementations are designed for the RC classifier. One uses parallel architecture and the other employs serial architecture. The size of the RC classifier in serial architecture is 38,000 gate count while the parallel architecture design requires 230,000 gate count. The circuit size of the hybrid network classifier is 606,000 gate count, even though the learning circuit is excluded in the design. The circuit size of the hybrid classifier is almost 2.6 times larger than that of the RC classifier, both use parallel architecture. Compared to the RC classifier in serial architecture, its size is 16 times bigger. Therefore the RC classifier is suitable for the hardware implementation even though the hybrid network classifier provides better performance.