| Title |
Person Recognition Using Gait and Face Features on Thermal Images |
| Authors |
김사문(Kim, Sa-Mun) ; 이대종(Lee, Dae-Jong) ; 이호현(Lee, Ho-Hyun) ; 전명근(Chun, Myung-Geun) |
| DOI |
https://doi.org/10.5370/KIEEP.2016.65.2.130 |
| Keywords |
Multimodal recognition ; Gait recognition ; Tenengrad ; Fusion recognition |
| Abstract |
Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method. |