A morphable model for the synthesis of 3D faces

コンピュータ・サイエンス : ACM computing surveys

Biometric Recognition - Security and Privacy Concerns.

With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.

A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

We developed a hybrid method for feature selection of classification tasks.Our hybrid method combines Artificial Bee Colony with Differential Evolution.We performed experiments over fifteen datasets from the UCI Repository.Our method selects good features without reducing accuracy of classification.By selecting features with our method, we reduced time required for classification. "Dimensionality" is one of the major problems which affect the quality of learning process in most of the machine learning and data mining tasks. Having high dimensional datasets for training a classification model may lead to have "overfitting" of the learned model to the training data. Overfitting reduces generalization of the model, therefore causes poor classification accuracy for the new test instances. Another disadvantage of dimensionality of dataset is to have high CPU time requirement for learning and testing the model. Applying feature selection to the dataset before the learning process is essential to improve the performance of the classification task. In this study, a new hybrid method which combines artificial bee colony optimization technique with differential evolution algorithm is proposed for feature selection of classification tasks. The developed hybrid method is evaluated by using fifteen datasets from the UCI Repository which are commonly used in classification problems. To make a complete evaluation, the proposed hybrid feature selection method is compared with the artificial bee colony optimization, and differential evolution based feature selection methods, as well as with the three most popular feature selection techniques that are information gain, chi-square, and correlation feature selection. In addition to these, the performance of the proposed method is also compared with the studies in the literature which uses the same datasets. The experimental results of this study show that our developed hybrid method is able to select good features for classification tasks to improve run-time performance and accuracy of the classifier. The proposed hybrid method may also be applied to other search and optimization problems as its performance for feature selection is better than pure artificial bee colony optimization, and differential evolution.

A hybrid approach of differential evolution and artificial bee colony for feature selection

Spiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which were inspired by inhibitory impulses/spikes or inhibitory synapses. In this work, we consider normal forms of ASN P systems. Specifically, we prove that ASN P systems with pure spiking rules of categories (a, a) and (a, a) without forgetting rules are universal as number generating devices. In an ASN P system with spiking rules of categories (a, a) and (a, a) without forgetting rules, the neurons change spikes to anti-spikes or change anti-spikes to spikes; such systems are proved to be universal. We also prove that ASN P systems with inhibitory synapses using pure spiking rules of category (a, a) and forgetting rules are universal. These results answer an open problem and improve a corresponding result from [IJCCC, IV(3), 2009, 273-282].

Normal Forms of Spiking Neural P Systems With Anti-Spikes

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios.

/pdf/intelligent-evacuation-management-systems-a-review-38uata6spo.pdf

Intelligent Evacuation Management Systems: A Review

Proposing a fully automatic 3D facial expression recognition framework.Investigating the capability of conformal mapping in the field of expression recognition.Enhancing the probability estimation of SVM for expression recognition.The results outperformed the previous studies significantly. Facial expressions are a powerful tool that communicates a person's emotional state and subsequently his/her intentions. Compared to 2D face images, 3D face images offer more granular cues that are not available in the 2D images. However, one major setback of 3D faces is that they impose a higher dimensionality than 2D faces. In this paper, we attempt to address this problem by proposing a fully automatic 3D facial expression recognition model that tackles the high dimensionality problem in a twofold solution. First, we transform the 3D faces into the 2D plane using conformal mapping. Second, we propose a Differential Evolution (DE) based optimization algorithm to select the optimal facial feature set and the classifier parameters simultaneously. The optimal features are selected from a pool of Speed Up Robust Features (SURF) descriptors of all the prospective facial points. The proposed model yielded an average recognition accuracy of 79% using the Bosphorus database and 79.36% using the BU-3DFE database. In addition, we exploit the facial muscular movements to enhance the probability estimation (PE) of Support Vector Machine (SVM). Joint application of feature selection with the proposed enhanced PE (EPE) yielded an average recognition accuracy of 84% using the Bosphorus database and 85.81% using the BU-3DFE database, which is statistically significantly better (at p < 0.01 and p < 0.001 , respectively) if compared to the individual exploit of the optimal features only.

Towards a robust affect recognition

Gait recognition algorithms often perform poorly because of low resolution video sequences, subjective human motion and challenging outdoor scenarios. Despite these challenges, gait recognition research is gaining momentum due to increasing demand and more possibilities for deployment by the surveillance industry. Therefore every research contribution which significantly improves this new biometric is a milestone. We propose a probabilistic sub-gait interpretation model to recognize gaits. A sub-gait is defined by us as part of the silhouette of a moving body. Binary silhouettes of gait video sequences form the basic input of our approach. A novel modular training scheme has been introduced in this research to efficiently learn subtle sub-gait characteristics from the gait domain. For a given gait sequence, we get useful information from the sub-gaits by identifying and exploiting intrinsic relationships using Bayesian networks. Finally, by incorporating efficient inference strategies, robust decisions are made for recognizing gaits. Our results show that the proposed model tackles well the uncertainties imposed by typical covariate factors and shows significant recognition performance.

Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics

Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

/pdf/uncertainty-in-a-spatial-evacuation-model-5c6kotkj2j.pdf

Ibrahim Venkat

Papers

Normal Forms of Spiking Neural P Systems With Anti-Spikes

Intelligent Evacuation Management Systems: A Review

Towards a robust affect recognition

Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics

Uncertainty in a spatial evacuation model