35096806
Human Face Identification from Video Based on Frequency Domain Asymmetry Representation Using Hidden Markov Models
In this paper we introduce a novel human face identification scheme from video data based on a frequency domain representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
10906898
The Role of Statistical Models in Biometric Authentication
The current paper demonstrates the role of statistical models in authentication tasks – both in system development and in performance evaluation. We first introduce a model-based face authentication system based on the Fourier domain phase using Gaussian Mixture Models (GMM) which yields verification error rates as low as 0.3% on a face database of 65 individuals with extreme illumination variations. We then present a statistical framework for predicting authentication error rates for future populations in a rigorous way. This is in contrast to most evaluation protocols used today that are based on observational studies and valid only for the databases at hand. Applications establish that our model-based approach has better predictive performance than an existing state-of-the-art authentication technique.
35384166
Facial asymmetry quantification for expression invariant human identification
We investigate facial asymmetry as a biometric under expression variation. For the first time, we have defined two types of quantified facial asymmetry measures that are easily computable from facial images and videos. Our findings show that the asymmetry measures of automatically selected facial regions capture individual differences that are relatively stable to facial expression variations. More importantly, a synergy is achieved by combining facial asymmetry information with conventional EigenFace and FisherFace methods. We have assessed the generality of these findings across two publicly available face databases: Using a random subset of 110 subjects from the FERET database, a 38% classification error reduction rate is obtained. Error reduction rates of 45-100% are achieved on 55 subjects from the Cohn-Kanade AU-Coded Facial Expression Database. These results suggest that facial asymmetry may provide complementary discriminative information to human identification methods, which has been missing in automatic human identification.
12094942
Design of redundant systems protected against common-mode failures
Redundancy techniques like duplication and Triple Modular Redundancy (TMR) are widely used for designing dependable systems to ensure high reliability and data integrity. In this paper, for the first time, we develop fault models for common-mode failures (CMFs) in redundant systems and describe techniques to design redundant systems protected against the modeled CMFs. We first develop an input-register-CMF model that targets systems with register-files. This paper shows that, in the presence of input-register-CMFs, we can always design duplex or TMR systems that either produce correct outputs or indicate error situations when incorrect outputs are produced. This property ensures data integrity. Next, we extend the input-register-CMF model to consider systems where the storage elements of the registers are not organized in register-files; instead, the register flip-flops are placed using conventional CAD programs. For this case, we present a technique to synthesize redundant systems with guaranteed data integrity against the extended input-register-CMFs.
1158736
Specifying and proving properties of timed I/O automata in the TIOA toolkit
Timed I/O Automata (TIOA) is a mathematical framework for modeling and verification of distributed systems that involve discrete and continuous dynamics. TIOA can be used for example, to model a real-time software component controlling a physical process. The TIOA model is sufficiently general to subsume other models in use for timed systems. The TIOA toolkit, currently under development, is aimed at supporting system development based on TIOA specifications. The TIOA toolkit is an extension of the IOA toolkit, which provides a specification simulator, a code generator, and both model checking and theorem proving support for analyzing specifications. This paper focuses on modeling of timed systems with TIOA and the TAME-based theorem proving support provided in the toolkit, for proving system properties, including timing properties. Several examples are provided by way of illustration
206624338
Efficient design diversity estimation for combinational circuits
Redundant systems are designed using multiple copies of the same resource (e.g., a logic network or a software module) in order to increase system dependability: Design diversity has long been used to protect redundant systems against common-mode failures. The conventional notion of diversity relies on "independent" generation of "different" implementations of the same logic function. In a recent paper, we presented a metric to quantify diversity among several designs. The problem of calculating the diversity metric is NP-complete (i.e., can be of exponential complexity). In this paper, we present efficient techniques to estimate the value of the design diversity metric. For datapath designs, we have formulated very fast techniques to calculate the value of the metric by taking advantage of the regularity in the datapath structures. For general combinational logic circuits, we present an adaptive Monte-Carlo simulation technique for estimating accurate bounds on the value of the metric.
64067670
Soft Error Resilient System Design through Error Correction
33350706
Evolutionary Modular MLP with Rough Sets and ID3 Algorithm for Staging of Cervical Cancer
This paper describes a way of designing a hybrid system for detecting the different stages of cervical cancer. Hybridisation includes the evolution of knowledge-based subnetwork modules with GAs using rough set theory and the ID3 algorithm. Crude subnetworks for each module are initially obtained via rough set theory and the ID3 algorithm. These subnetworks are then combined, and the final network is evolved using genetic algorithms. The evol-ution uses a restricted mutation operator which utilises the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conven-tional MLP. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for rule extraction.