Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we introduce Flexible Biometric Recognition (FBR), a novel framework designed to advance conventional face, periocular, and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the Vision Transformer architecture. MFA facilitates the fusion of modalities, ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally, MPT serves as a unifying bridge, intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular, with a specific emphasis on the ocular region, yielding exceptional performance in both intra-and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the note-worthy performance of the FBR model. The source code is available at https://github.com/MIS-DevWorks/FBR.

Traditional biometrics identification performs matching between probe and gallery that may involve the same single or multiple biometric modalities. This paper presents a cross-matching scenario where the probe and gallery are from two distinctive biometrics, i.e., face and periocular, coined as face-periocular cross-identification (FPCI). We propose a novel contrastive loss tailored for face-periocular cross-matching to learn a joint embedding, which can be used as a gallery or a probe regardless of the biometric modality. On the other hand, a hybrid attention vision transformer is devised. The hybrid attention module performs depth-wise convolution and conv-based multi-head self-attention in parallel to aggregate global and local features of the face and periocular biometrics. Extensive experiments on three benchmark datasets demonstrate that our model sufficiently improves the performance of FPCI. Besides that, a new face-periocular dataset in the wild, the Cross-modal Face-periocular dataset, is developed for the FPCI models training.

Recurrent neural networks (RNNs) have been widely used for complex data modeling. However, when it comes to long-term time-dependent complex sequential data modeling with stochasticities, RNNs seem to fail because of vanishing gradients problem. Hence, in this paper, we propose a new architecture, stochastic long short term memory (S-LSTM), along with its forward and backward dynamics equations. S-LSTM models stochasticities using Bayesian brain hypothesis, which is a probabilistic model that makes predictions against which samples are tested to update the conclusions about their causes. This is the same as minimizing the difference between inference and posterior densities for suppressing the free energy. During training of S-LSTM, it predicts the mean as well as variance at each time step. The prediction error is minimized by the predicted variance which acts as an inverse weighting factor for prediction error and tries to optimize the maximum likelihood. Our proposed model is evaluated through numerical experiments on noisy Lissajous curves. In the experiments, S-LSTM is found to predict and preserve more stochasticities in the noisy Lissajous curves as compared to LSTM.

Episodic memory which can store and recall episodes has been modeled by various research. Those models focus on encoding and retrieving the same sequence of events of episodes. In this paper, we propose context preference-based deep adaptive resonance theory (CPD-ART). CPD-ART uses a new approach in encoding and retrieving a temporal sequence of events considering subjects, preference criteria such as weather, and object contexts such as beverage. A new layer, context preference field, is added to the encoding and retrieval processes for decision making. Context preference field encodes and stores the knowledge of criteria and object contexts, along with their relations in probability weight vectors. Simulation results demonstrate that CPD-ART is able to conduct decision making analysis and retrieve the sequence of events of an episode correctly through decision making analysis based on subjects, preference criteria, and the object contexts.

Various kinds of evolutionary algorithms have been developed to solve multi-objective optimization problems. One of them is multi-objective quantum-inspired evolutionary algorithm (MQEA) which utilizes quantum computing concepts to search the solution space effectively. MQEA used nondominated sorting and crowding distance calculation as the selection operator. This paper proposes MQEA with another kind of selection operator. The proposed RN-MQEA uses reference point-based nondominated sorting approach as the selection operator, which is adopted from NSGA-III. In the computer simulations, RN-MQEA is found to provide more diverse solutions compared to MQEA and NSGA-III in solving the DTLZ test problems.