Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.     

A two visual systems approach to understanding voice and gestural interaction

It is important to consider the physiological and behavioral mechanisms that allow users to physically interact with virtual environments. Inspired by a neuroanatomical model of perception and action known as the two visual systems hypothesis, we conducted a study with two controlled experiments to compare four different kinds of spatial interaction: (1) voice-based input, (2) pointing with a visual cursor, (3) pointing without a visual cursor, and (4) pointing with a time-lagged visual cursor. Consistent with the two visual systems hypothesis, we found that voice-based input and pointing with a cursor were less robust to a display illusion known as the induced Roelofs effect than pointing without a cursor or even pointing with a lagged cursor. The implications of these findings are discussed, with an emphasis on how the two visual systems model can be used to understand the basis for voice and gestural interactions that support spatial target selection in large screen and immersive environments.     

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m² environment.     

Estimation of complex multimodal motion: an approach based on robust statistics and Hough transform

An application of robust statistics in a Hough transform based motion estimation approach is presented. The algorithm is developed and experiments are performed, proving its superior performance in terms of estimate accuracy, convergence, robustness and better segmentation. Comparative results with standard methods are also included.     

Spontaneous facial expression recognition: A robust metric learning approach

Spontaneous facial expression recognition is significantly more challenging than recognizing posed ones. We focus on two issues that are still under-addressed in this area. First, due to the inherent subtlety, the geometric and appearance features of spontaneous expressions tend to overlap with each other, making it hard for classifiers to find effective separation boundaries. Second, the training set usually contains dubious class labels which can hurt the recognition performance if no countermeasure is taken. In this paper, we propose a spontaneous expression recognition method based on robust metric learning with the aim of alleviating these two problems. In particular, to increase the discrimination of different facial expressions, we learn a new metric space in which spatially close data points have a higher probability of being in the same class. In addition, instead of using the noisy labels directly for metric learning, we define sensitivity and specificity to characterize the annotation reliability of each annotator. Then the distance metric and annotators' reliability is jointly estimated by maximizing the likelihood of the observed class labels. With the introduction of latent variables representing the true class labels, the distance metric and annotators' reliability can be iteratively solved under the Expectation Maximization framework. Comparative experiments show that our method achieves better recognition accuracy on spontaneous expression recognition, and the learned metric can be reliably transferred to recognize posed expressions.     

A novel approach for multimodal facial expression recognition using deep learning techniques

Multimedia Tools and Applications - Nowadays, digital protection has become greater prominence for daily digital activities. It’s far vital for people to keep new passwords in their minds and...     

Flexible and robust multimodal interfaces for universal access

Multimodal interfaces are inherently flexible, which is a key feature that makes them suitable for both universal access and next-generation mobile computing. Recent studies also have demonstrated that multimodal architectures can improve the performance stability and overall robustness of the recognition-based component technologies they incorporate (e.g., speech, vision, pen input). This paper reviews data from two recent studies in which a multimodal architecture suppressed errors and stabilized system performance for accented speakers and during mobile use. It concludes with a discussion of key issues in the design of future multimodal interfaces for diverse user groups.     

A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition

This study proposes a novel deep learning approach for the fusion of 2D and 3D modalities in in-the-wild facial expression recognition (FER). Different from other studies, we exploit the 3D facial information in in-the-wild FER. In particular, in-the-wild 3D FER dataset is not widely available; therefore, 3D facial data are constructed from available 2D datasets thanks to recent advances in 3D face reconstruction. The 3D facial geometry features are then extracted by deep learning technique to exploit the mid-level details, which provides meaningful expression for the recognition. In addition, to demonstrate the potential of 3D data on FER, the 2D projected images of 3D faces are taken as additional input to FER. These features are then jointly fused with 2D features obtained from the original input. The fused features are then classified by support vector machines (SVMs). The results show that the proposed approach achieves state-of-the-art recognition performances on Real-World Affective Faces (RAF) and Static Facial Expressions in the Wild (SFEW 2.0), and AffectNet dataset. This approach is also applied to a 3D FER dataset, i.e. BU-3DFE, to compare the effectiveness of reconstructed and available 3D face data for FER. This is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.     

A singular value decomposition representation based approach for robust face recognition

In the field of face recognition, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been widely used. Although both SRC and CRC have shown good classification results, it is still controversial whether it is sparse representation or collaborative representation that helps face recognition. In this paper, a new singular value decomposition based classification (SVDC) is proposed for face recognition. The proposed approach performs SVD on the training data of each class, and then determines the class of a test sample by comparing in which class of singular vectors it can be better represented. Experimental results on Yale B, PIE and UMIST datasets show that the proposed method achieves better recognition performance compared with several existing representation based classification algorithms. In addition, by adding Gaussian noise and Salt pepper noise to these datasets, it is proved that SVDC has better robustness. At the same time, the experimental results show that the recognition accuracy of the method acting on the training samples constructed by each class is higher than that of the method acting on the training sets constructed by all classes.

     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

多模态情感识别与理解发展现状及趋势

情感计算是人工智能领域的一个重要分支,在交互、教育、安全和金融等众多领域应用广泛。单纯依靠语音、视频单一模态的情感识别并不符合人类对情感的感知模式,在受到干扰的情况下识别准确率会迅速下降。为了充分挖掘不同模态数据的互补性,多模态融合的情感识别研究正日益受到研究人员的广泛重视。本文分别从多模态情感识别概述、多模态情感识别与理解、抑郁症情感障碍检测及干预 3 个维度介绍多模态情感计算研究现状。本文认为具备可扩展性的情感特征设计、基于大模型迁移学习的识别方法将是未来的发展方向,并在解决抑郁、焦虑等情感障碍方面的作用日益凸显。     

A contribution to a multimodal approach to knowledge modeling

At present a steep increase in efforts by human beings in search of information take place. Pattern Recognition extracts information from the real world. It is a tool for modelling and real-world learning. Thus it requires precise knowledge of the objectives to be achieved with the information it produces. Human senses are part of their interface with the environment. Our senses are signal encoders according some representation that is understood by the central nervous system. Multimodality in signal capturing makes the spectrum of activities of intelligent agents wider. This paper aims to stimulate research for providing multimodal information, say, information that can be captured by different senses. This technique can improve decision-making; promote the inclusion of sense disabled individuals, giving higher amplitude for usefulness of Pattern Recognition. It considers multimodal display of knowledge to practical feasibility of knowledge presentation adapted to human sensing and perception towards decision improvement and inclusion of perception-impaired people.     

A structural approach to robust control

We consider the problem of stabilizing linear stationary parametrically uncertain systems with a guaranteed stability margin. The methodology of our approach is based on the synthesis of superstable closed systems, done with the procedures derived from the block control principle and their modifications, the procedures consisting of sequential establishment of local connections in elementary blocks that provide for the superstability of each block and the closed system as a whole in the new coordinate basis. The fact that the notion of superstability is formulated in terms of the elements of the system matrix based on inequalities lets us provide for robust stability for all admissible values of indefinite parameters in such systems. The robust control algorithms that we have developed are applicable to a practically significant class of linear systems which, given that parameters change in known ranges, preserve structural controllability properties defined by the nominal system.     

An approach to robust control design

A robust stability test is formulated and the methodology of its use in the robust control system design is presented. The paper makes a contribution to the existing approaches to solution of this class of problems.     

New approach to robust observer design

This paper shows that based on the recent development of observer design solution, an observer pole selection method can be formulated to minimize the observer gain to the system input. It is proved that this method is a deterministic approach to the recovery of the loop transfer function and robustness of direct state feedback systems.