• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.94-97
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• 2003

In this Paper, propose for MLP(multilayer perception) neural network that uses optimization recognition training scheme for the wavelet transform and the numeral image add to noise, and apply this system in Numeral Recognition. As important part of original image information preserves maximum using the wavelet transform, node number of neural network and the loaming convergence time did size of input vector so that decrease. Apply in training vector, examine about change of the recognition rate as optimization recognition training scheme raises noise of data gradually. We used original image and original image added 0, 10, 20, 30, 40, 50㏈ noise (or the increase of numeral recognition rate. In case of test image added 30∼50㏈, numeral recognition rate between the original image and image added noise for training Is a little But, in case of test image added 0∼20㏈ noise, the image added 0, 10, 20, 30, 40 , 50㏈ noise is used training. Then numeral recognition rate improved 9 percent.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.257-260
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• 2001

We propose The Korean digit recognition system using speech and image information. In the experiments, we investigate that image information affect recognition rate. Recognition rate of teamed data and testing data show 100%, 78% each other.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.217-223
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• 2009

Vocabulary recognition system of Mobile terminal is executed statistical method for vocabulary recognition and used statistical grammar recognition system using N-gram. If limit arithmetic processing capacity in memory of vocabulary to grow then vocabulary recognition algorithm complicated and need a large scale search space and many processing time on account of impossible to process. This study suggest vocabulary recognition optimize using MLHF System. MLHF separate acoustic search and lexical search system using FLaVoR. Acoustic search feature vector of speech signal extract using HMM, lexical search recognition execution using Levenshtein distance algorithm. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%, represent recognition speed of 1.61 second.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.34-40
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• 2006

1. Objectives We are going to develope 3D Face Recognition Apparatus to analyse the facial characteristics of the Sasangin. In the process, we should identify the recognition rate of the three dimensional position using this Apparatus. 2. Methods We took a photograph of calibrator($280{\times}400mm$) with interval of 20mm longitudinal direction of 10 times using 3D Face Recognition Apparatus. In the practice, we obtained 967 point to the exclusion of points deviating from the visual field of dual camera. And we made a comparison between measurement values and three dimensional standard values to calculate the errors. 3. Results and Conclusions In this test, the average error rate of X axis values was 0.019% and the maximum error rate of X axis values was 0.033%, the average error rate of Y axis values was 0.025% and the maximum error rate of Y axis values was 0.044%, the average error rate of Z axis values was 0.158% and the maximum error rate of Z axis values was 0.269%. This results exhibit much improvement upon the average error rate 1% and the maximum error rate 2.242% of the existing 3D Recognition Apparatus. In conclusion, we assessed that this apparatus was adaptable to abstract the facial characteristic point from three dimensional face shape in the mechanical aspects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.9-16
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• 2005

In this paper, we compare face recognition rate by distance change using Principal Component Analysis algorithm being input left and right image in stereo image. Change to YCbCr color space from RGB color space in proposed method and face region does detection. Also, after acquire distance using stereo image extracted face image's extension and reduce do extract robust face region, experimented recognition rate by using PCA algorithm. Could get face recognition rate of 98.61%(30cm), 98.91%(50cm), 99.05%(100cm), 99.90%(120cm), 97.31%(150cm) and 96.71%(200cm) by average recognition result of acquired face image. Therefore, method that is proposed through an experiment showed that can get high recognition rate if apply scale up or reduction according to distance.