Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition
This work addresses a specific bottleneck in face recognition using light field images, offering an incremental improvement for that domain.
The paper tackled the problem of processing multiple dependent sequences simultaneously in LSTM networks by proposing two novel LSTM cell architectures that jointly learn from such sequences, resulting in improved face recognition performance over state-of-the-art methods on the IST-EURECOM LFFD dataset with three challenging protocols.
Long Short-Term Memory (LSTM) is a prominent recurrent neural network for extracting dependencies from sequential data such as time-series and multi-view data, having achieved impressive results for different visual recognition tasks. A conventional LSTM network can learn a model to posteriorly extract information from one input sequence. However, if two or more dependent sequences of data are simultaneously acquired, the conventional LSTM networks may only process those sequences consecutively, not taking benefit of the information carried out by their mutual dependencies. In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks. The efficacy of the novel LSTM cell architectures is assessed by integrating them into deep learning-based methods for face recognition with multi-view, light field images. The new cell architectures jointly learn the scene horizontal and vertical parallaxes available in a light field image, to capture richer spatio-angular information from both directions. A comprehensive evaluation, with the IST-EURECOM LFFD dataset using three challenging evaluation protocols, shows the advantage of using the novel LSTM cell architectures for face recognition over the state-of-the-art light field-based methods. These results highlight the added value of the novel cell architectures when learning from correlated input sequences.