A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.

/pdf/face-recognition-with-radial-basis-function-rbf-neural-17p2mq3yfx.pdf

Face recognition with radial basis function (RBF) neural networks

This paper presents a high-performance LED lamp driver with an improved single-stage Flyback configuration. A constant current control method is used to regulate the lamp current and brightness. A laboratory prototype has been built and tested. With the prototype, high efficiency, high power factor, and constant lamp current can be achieved under different pieces of LED series connection. Copyright © 2007 John Wiley & Sons, Ltd.

An improved single-stage Flyback PFC converter for high-luminance lighting LED lamps

Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important intermediary step for many subsequent face processing operations that range from biometric recognition to the understanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their progress over the last decade, categorize them and show comparative performance statistics of the state of the art. We discuss the main trends and indicate current shortcomings with the expectation that this survey will provide further impetus for the much needed high-performance, real-life face landmarking operating at video rates.

/pdf/a-comparative-study-of-face-landmarking-techniques-3a6rp0iin0.pdf

A comparative study of face landmarking techniques

We developed a system that automatically authenticates offine handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offine. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.

/pdf/offline-signature-verification-using-the-discrete-radon-1lu2x50a0v.pdf

Offline signature verification using the discrete radon transform and a hidden Markov model

In this paper, we propose an enhanced multimodal personal authentication system for mobile device security. The proposed approach fuses information obtained from face, teeth and voice modalities to improve performance. To integrate three modalities, we employ various fusion techniques such as the weighted-summation rule, K-NN, Fisher and Gaussian classifiers, and we then evaluate the authentication performance of the proposed system. The performance is evaluated on a database consisting of 1000 biometric traits that correspond to the face, teeth and voice modalities of 50 persons, i.e., 20 biometric traits per individual, in which these biometric traits are simultaneously collected by a smart-phone device. The experiment results integrating the three modalities showed the error rates of 1.64%, 4.70%, 3.06% and 1.98% for the weighted-summation rule, K-NN, Fisher and Gaussian classifier, respectively, and that the weight-summation rule outperformed the other classification approaches. In contrast, the error rates regarding a single modality were 5.09%, 7.75% and 8.98% for face, teeth, and voice modalities, respectively. From these results, we confirmed that the proposed method achieved a significant performance improvement over the methods using a single modality, and the results showed that the proposed method was very effective through various fusion experiments.

Person authentication using face, teeth and voice modalities for mobile device security

The recognition of a car's license plate for an automatic parking system is important for identifying the car at the entrance of the parking area because the car license plate has unique information for each car. This paper proposes the recognition of car license plate which is accurate and robust to environmental variation by using the car's license plate patterns according to motor vehicle regulation and a 4-layer BP neural network with supervised learning. In this method, the candidates regions of the car license plate are determined approximately according to the car license plate regulation such as color, the ratio and shape of the car license plate, the pattern of characters and numbers etc. For the results of recognition by neural networks, the candidate that has characters and numbers according to motor vehicle regulation is certified as license-plate region. Since the results of characters-pattern recognition are used to certify the license plate, the ability of license plate extraction is more accurate and the car can be identified simultaneously. The experimental results of seventy car images with the prototype of the automatic parking system show the performance of car license plate extraction rate of 96%, and the recognition rate is 92%.

https://www.kmutt.ac.th/rippc/pdf/423033.pdf

The recognition of car license plate for automatic parking system

A single-stage high-power-factor electronic ballast with a Class-E rectifier as a power-factor corrector is proposed. A Class-E rectifier is inserted between the front-end bridge rectifier and the bulk filter capacitor to increase the conduction angle of the bridge-rectifier diode current for obtaining low line-current harmonics. The Class-E rectifier is driven by a high-frequency sinusoidal current source, which is obtained from the square-wave output voltage of the Class-D inverter through an LC series resonant circuit. A high-frequency transformer is used for impedance matching. The experimental results for a 32-W prototype ballast are given. The switching frequency was 61.3 kHz. At full power, the power factor was 0.992 and the total ballast efficiency was 88.3%. The lamp-current crest factor was about 1.36. The simulated and experimental results were in very good agreement.

Single-stage electronic ballast with class-E rectifier as power-factor corrector

Face detection and facial feature localization without considering the appearance of image context

This article describes a method of an off-line signature recognition by using the Hough transform to detect stroke lines from the signature image. The Hough transform is used to extract the parameterized Hough space from the signature skeleton as a unique characteristic feature of signatures. In the experiment, the backpropagation neural network is used as a tool to evaluate the performance of the proposed method. The system has been tested with 70 test signatures from different persons. The experimental results reveal a recognition rate 95.24%.

https://www.kmutt.ac.th/rippc/pdf/423040.pdf

Off-line signature recognition using parameterized Hough transform

This paper proposes the integration of moment invariant and PCA for varied-pose face recognition. Firstly, the global feature is extracted by PCA for determine the minimum error. If error less than threshold, system will accepts the classification result from PCA. On the other hand, the system will rejects and moment invariant is used to analyze the local face such as nose and eyes. In the experiments, ORL face database is used to perform the method yielded the recognition rate of 96%.

Face recognition system with PCA and moment invariant method

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