심층 신경회로망 앙상블을 이용한 걸음걸이 인식에 대한 연구

Abstract

The recognition of a person from his (her) gait has been a recent focus in computer vision because of its unique advantages such as non-invasive and human friendly. Gait recognition, however, has the weakness that it is not reliable compared with other biometrics. In this paper, we applied deep neural network ensemble to the gait recognition problem. The deep neural network ensemble is a learning paradigm where a collection of deep neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single deep neural network such as convolution neural network and recurrent neural network. To increase reliability of the gait recognition, gait energy image (GEI) and Motion silhouette image (MSI) are extracted for gait features and convolution and recurrent neural network ensemble are used for classifier. Experiments are performed with the NLPR and SOTON databases to show the efficiency of the proposed algorithm. The performance of proposed method is 4.55%, 4.85%, 2.5% and 2.43% better than single CNN, respectively in two databases. As a result we can create a recognition system with accuracy of 100%, 100%, and 94% in the NLPR database and 97.35% in the SOTON database.