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
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.
2734062
A design diversity metric and reliability analysis for redundant systems
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. This concept is qualitative and does not provide a basis to compare the reliabilities of two diverse systems. In this paper, for the first time, we present a metric to quantify diversity among several designs. Based on this metric, we derive analytical reliability models that show a simple relationship between design diversity, system failure rate, and mission time. In addition, we present simulation results to demonstrate the effectiveness of design diversity in Duplex and Triple Modular Redundant (TMR) systems. For independent multiple-module failures, we show that, mere use of different implementations does not always guarantee higher reliability compared to redundant systems with identical implementations-it is important to analyze the reliability of redundant systems using our metric. For common-mode failures and design faults, there is a significant gain in using different implementations-however, as our analysis shows, the gain diminishes as the mission time increases. Our simulation results also demonstrate the usefulness of diversity for enhancing the self-testing properties of redundant systems.
28682159
Delay defect screening using process monitor structures
This paper presents delay test data collected from test chips fabricated in a 0.18 /spl mu/ technology. The experimental data shows that process monitor structures such as on-chip ring oscillators are effective in identifying slow parts while performing transition fault testing at frequencies slower than the rated frequency.
3453269
Combinational logic synthesis for diversity in duplex systems
We describe logic synthesis techniques for designing diverse implementations of combinational logic circuits in order to maximize the data integrity of diverse duplex systems in the presence of common-mode failures. Data integrity means that the system either produces correct outputs or indicates errors when incorrect outputs we produced. Design diversity has long been used to increase the data integrity of duplex systems against common-mode failures. The conventional notion of diversity is qualitative and relies on "independent" generation of "different" implementations. In a recent paper, we presented a metric to quantify, diversity among several designs. Our synthesis techniques described in this paper use the diversity metric as a cost function and maximize diversity while reducing the area overhead of the resulting diverse duplex system.
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.
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.