Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Abstract

In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method

“…When the established HAM model, which stores biometric fusion features of all authorized users, receives a face pattern vector of an unauthorized user, there will exist a forecasting fingerprint pattern output of the visitor. In [32], the input pattern and forecasting output pattern are the same biometric pattern. It uses the AAM network structure, which fuses the face input and the same face output, but it cannot achieve the fusion of different biological models.…”

“…The HAM model, which stores coupling information based on input-output patterns, can recall a stored output pattern by receiving a different input pattern. In [32], to protect the face features database fundamentally, a new face recognition method by AAM based on RNNs is proposed without establishing a face feature database, in which the face features are transformed into the parameters of the AAM model. We notice that the HAM models can construct the association between the input and output patterns in a robust way, and this association can be regarded as feature fusion of two different kinds of patterns.…”