Equity Multi-Instance Face Identification Based on Threshold Control and Fusion Feature
Deng Jian · 2013
Image retrieval based on multi-instance learning (MIL) has great value in the field of regional image retrieval.The traditional voting mechanism in MIL is prone to misunderstanding,because the local similarity does not mean the overall similarity in face identification.Firstly,instance equity concepts of equity MIL are presented.Each kind of instance has different equity and the training set has the similar features to the test set.Therefore,the classification attribution for different packets can be obtained by the sum of results from multiplying every discriminant result and its instance equity.Secondly,the overall characteristic is considered as a special instance,and the overall sample equity threshold is used to control equity ratio.At the same time,the abnormal conditions,such as two persons have similar facial features,are prevented by means of the feature fusion.And the recognition rate is improved by the use of threshold control.The experimental results on the ORL and FERET show that the algorithm is feasible and the performance is superior to other algorithms.