206590610
Modeling Phase Spectra Using Gaussian Mixture Models for Human Face Identification
It has been established that information distinguishing one human face from another is contained to a large extent in the Fourier domain phase component of the facial image. However, to date, formal statistical models for this component have not been deployed in face recognition tasks. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) for the phase component for performing human identification. Classification and verification are performed using a MAP estimate and we show that we are able to achieve identification error rates as low as 2% and verification error rates as low as 0.3% on a database with 65 individuals with extreme illumination variations. The proposed method is easily able to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. A potential use of the method in illumination normalization is also discussed.
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.
45398591
A One Bit Facial Asymmetry Code (FAC) in Fourier Domain for Human Recognition
The present paper introduces a novel set of biometrics based on facial asymmetry measures in the frequency domain using a compact one-bit representation. A simplistic Hamming distance-type classifier is proposed as a means for matching bit patterns for identification purposes which is more efficient than PCA-based classifiers from storage and computation point of view, and produces equivalent results. A comparison with spatial intensity-based asymmetry measures suggests that our proposed measures are more robust to intra-personal distortions with a misclassification rate of only 4.24% on the standard facial expression database (Cohn-Kanade) consisting of 55 individuals. In addition, a rigorous statistical analysis of the matching algorithm is presented. The role of asymmetry of different face parts (e.g., eyes, mouth, nose) is investigated to determine which regions provide the maximum discrimination among individuals under different expressions.
8080205
Using Feature Combination and Statistical Resampling for Accurate Face Recognition Based on Frequency Domain Representation of Facial Asymmetry
This paper explores the efficiency of facial asymmetry in face identification tasks using a frequency domain representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and random subspace method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of frequency domain representation of facial asymmetry in automatic identification tasks
62742629
Local facial asymmetry for expression classification
We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. Quantitative evaluations of expression classification using local asymmetry demonstrate statistically significant improvements over expression classification results on the same data set without explicit representation of facial asymmetry. A comparison of discriminative local facial asymmetry features for expression classification versus human identification is given.
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.
29308229
Safety Verification of Model Helicopter Controller Using Hybrid Input/Output Automata
This paper presents an application of the Hybrid I/O Automaton (HIOA) framework [12] in verifying a realistic hybrid system. A supervisory pitch controller for a model helicopter system is designed and then verified. The design of the supervisor is limited by the actuator bandwidth, the sensor inaccuracies, and the sampling rates. Verification is carried out by induction over the length of an execution of the composed system automaton. The HIOA model makes the inductive proofs tractable by decomposing them into independent discrete and continuous parts. The paper also presents a set of language constructs for specifying hybrid I/O automata.
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.
900289
Techniques and algorithms for fault grading of FPGA interconnect test configurations
Conventional fault simulation techniques for field programmable gate arrays (FPGAs) are very complicated and time consuming. The alternative, FPGA fault emulation technique, is incomplete and can be used only after the FPGA chip is manufactured. In this paper, we present efficient algorithms for computing the fault coverage of a given FPGA test configuration. The faults considered are opens and shorts in FPGA interconnects. The presented technique is able to report all detectable and undetectable faults and, compared with conventional methods, is orders of magnitude faster.
13253511
Verifying Average Dwell Time by Solving Optimization Problems
A switched system is a hybrid system whose discrete mechanisms are abstracted away in terms of an exogenous switching signal which brings about the mode switches. For switched systems, the Average Dwell time (ADT) property defines restricted classes of switching signals which can be used for proving stability. In this paper, we develop optimization-based methods for automatically verifying ADT properties of hybrid systems. This enables us to prove stability of hybrid systems, provided the individual modes of the system are stable. For two special classes of hybrid systems, we show that the resulting optimization problems can indeed be solved efficiently using standard mathematical programming techniques. We also present simulation relation-based proof methods for establishing equivalence of hybrid systems with respect to ADT. The proposed methods are applied to verify ADT properties of a linear hysteresis switch and a nondeterministic thermostat.