• Journal of Intelligence and Information Systems
• /
• v.11 no.1
• /
• pp.95-105
• /
• 2005

As the security system using facial recognition, the recognition performance depends on the environments (e. g. face expression, hair style, age and make-up etc.) For the revision of easily changeable environment, it's generally used to set up the threshold, replace the face image which covers the threshold into images already registered, and update the face images additionally. However, this usage has the weakness of inaccuracy matching results or can easily active by analogous face images. So, we propose the genetic algorithm which absorbs greatly the facial similarity degree and the recognition target variety, and has excellence studying capacity to avoid registering inaccuracy. We experimented variable and similar face images (each 30 face images per one, total 300 images) and performed inherent face images based on ingredient analysis as face recognition technique. The proposed method resulted in not only the recognition improvement of a dominant gene but also decreasing the reaction rate to a recessive gene.

• The KIPS Transactions:PartB
• /
• v.15B no.5
• /
• pp.477-486
• /
• 2008

Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. To improve the face recognition rate, we propose a normalization method of facial expression to diminish the difference of facial expression between probe and gallery faces. Two approaches are used to facial expression modeling and normalization from single still images using a generic facial muscle model without the need of large image databases. The first approach estimates the geometry parameters of linear muscle models to obtain a biologically inspired model of the facial expression which may be changed intuitively afterwards. The second approach uses RBF(Radial Basis Function) based interpolation and warping to normalize the facial muscle model as unexpressed face according to the given expression. As a preprocessing stage for face recognition, these approach could achieve significantly higher recognition rates than in the un-normalized case based on the eigenface approach, local binary patterns and a grey-scale correlation measure.

• The Journal of the Acoustical Society of Korea
• /
• v.9 no.4
• /
• pp.66-76
• /
• 1990

In this work, the effect of noise and distortion of a telephone channel on the speech recognition is studied, and methods to improve the recognition rate are proposed. Computer simulation is done using the 100-word test data whichwere made by pronouncing ten times 100-phonetically balanced Korean isolated words in a speaker dependent mode. First, a spectral subtraction method is suggested to improve the noisy speech recognition. Then, the effect of bandwidth limiting and channel distortion is studied. It has been found that bandwidth limiting and amplitude distortion lower the recognition rate significantly, but phase distortion affects little. To reduce the channel effect, we modify the reference pattern according to some training data. When both channel noise and distortion exist, the recognition rate without the proposed method is merely 7.7~26.4%, but the recognition rate with the proposed method is drastically increased to 76.2~92.3%.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.7
• /
• pp.55-62
• /
• 2001

In this paper, we propose a new adaptative pruning algorithm which effectively reduces the search space during the recognition process. As maximum probabilities between neighbor frames are highly interrelated, an efficient pruning threshold value can be obtained from the maximum probabilities of previous frames. The main idea is to update threshold at the present frame by a combination of previous maximum probability and hypotheses probabilities. As present threshold is obtained in on-going recognition process, the algorithm does not need any pre-experiments to find threshold values even when recognition tasks are changed. In addition, the adaptively selected threshold allows an improvement of recognition speed under different environments. The proposed algorithm has been applied to a Korean Address recognition system. Experimental results show that the proposed algorithm reduces the search space of average 14.4% and 9.14% respectively while preserving the recognition accuracy, compared to the previous method of using fixed pruning threshold values and variable pruning threshold values.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.4
• /
• pp.689-697
• /
• 2010

In this study, we propose a new procedure for improving the character recognition of text area extracted from video images. The recognition of strings extracted from video, which are mixed with Hangul, English, numbers and special characters, etc., is more difficult than general character recognition because of various fonts and size, graphic forms of letters tilted image, disconnection, miscellaneous videos, tangency, characters of low definition, etc. We improved the recognition rate by taking commonly used letters and leaving out the barely used ones instead of recognizing all of the letters, and then using SVM and RDA character recognition methods. Our numerical results indicate that combining SVM and RDA performs better than other methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.5
• /
• pp.1052-1061
• /
• 2003

In this paper, we propose the car plate recognition using SOM algorithm and describe the parking control system using the proposed car plate recognition. The recognition of car plate was investigated by means of the SOM algorithm. The morphological information of horizontal and vertical edges was used to extract a plate area from a car image. In addition, the 4-direction contour tracking algorithm was applied to extract the specific area, which includes characters from an extracted plate area. The extracted characteristic area was recognized by using the SOM algorithm. In this paper, 50 car images were tested. The extraction rate obtained by the proposed extraction method showed better results than that from the color information of RGB and HSI, respectively. And the car plate recognition using SOM algorithm was very efficient. We develop the parking control system using the proposed car plate recognition that shows performance improvement by the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4903-4929
• /
• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.152-159
• /
• 2010

Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.7C
• /
• pp.598-603
• /
• 2010

In this paper, we work for Lipreading using visual information for ship environment. Lipreading is studied for using image information including lips of a speaker at the existing speech recognition system. This technique is a compensation method to increase recognition rate decreasing remarkably in noisy circumstances. Proposed way improved the rate of recognition improving methode of preprocessing using the Gabor Filter for Ship Environment. The experiment were carried out under changing of light with time in the ship environment with lip image. For Comparing with recognition, make a compare with between method of lip region of interest (ROI) before Gabor filtering and after Gabor filtering. In the case of using method of lip ROI before Gabor filtering, the result of the experiments applying to the proposed ways recognition resulting in 44% of recognition.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.67-77
• /
• 2004

The recent research has been focused on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network(BMNN) is a multi-layer perception of 4 layers, each of which performs a certain level of abstraction of input features. In BMNN the third layer combines audio md visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition. Our research demonstrates that diverse sources of information need to be integrated to improve the accuracy of speech recognition particularly in noisy environments.