Face Recognition of Small-Scale Dataset Based on Joint Loss Functions

In order to solve the problems induced in face recognition with small-scale datasets that small data size along with large changes and over-fit trend during directly training with deep neural networks, a face recognition method was proposed based on small-scale datasets with joint loss functions. This method was arranged to finetune a pre-trained model trained with large-scale public facial datasets based on Softmax loss function to make full use of all parameters in the model and improve feature representation capability of the model. Compared with conventional feature postprocessing methods, the effectiveness of this method was verified and evaluated. Experiment results show that this method can largely improve the performance of face retrieval on school freshmen face dataset.