Face occlusion recognition based on MEBML
Ge Li · Journal of Jilin University · 2014
To solve the problem of face occlusion recognition,an algorithm,which combines MindEvolution-Based Machine Learning(MEBML)and local face feature,is proposed.First,a new method of LBP offset feature group extraction is presented to define a new feature contrast rule.By contrasting images between face occlusion and non-occlusion,the local area score of similarity is obtained.Second,similartaxis and dissimilation evolutionary operations are carried out for all local areas.Then,the areas of face occlusion and non-occlusion are obtained.Finally,if the non-occlusion area is large enough and centralized,the feature of this area can be used to recognize face even it is partially covered.Experiment results demonstrate that the new method can recognize 92% face occlusion images with low error ratio.