FINE-TUNING APPROACH TO NIR FACE RECOGNITION

Despite extensive researches for face recognition (FR), it is still difficult to apply deep CNN models to NIR FR due to a lack of training data. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to NIR FR with small training datasets. In the proposed approach, parameters of deep CNN models for RGB FR are utilized as initial parameters to train deep CNN models for NIR FR. The proposed approach has two main advantages: 1) High NIR FR performances can be achieved with very small public training datasets. 2) We can easily secure good generalization for NIR FR in various environments. Our fine-tuning approach achieved a validation rate of 99.70% with the PolyU-NIRFD database. In addition, we constructed private face databases with Intel (R) RealSense (TM) SR300. On the VF_NIR database, which is one of the private databases, we achieved a validation rate of 94.47%.