• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.69-80
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• 1999

Recently, social requirements of personal identification techniques are rapidly expanding in a number of new application ares. Especially fingerprint recognition is the most important technology. Fingerprint recognition technologies are well established, proven, cost and legally accepted. Therefore, it has more spot lighted among the any other biometrics technologies. In this paper we propose a new on-line fingerprint recognition algorithm for non-inked type live scanner to fit their increasing of security level under the computing environment. Fingerprint recognition system consists of two distinct structural blocks: feature extraction and feature matching. The main topic in this paper focuses on the feature matching using the fingerprint minutiae (ridge ending and bifurcation). Minutiae matching is composed in the alignment stage and matching stage. Success of optimizing the alignment stage is the key of real-time (on-line) fingerprint recognition. Proposed alignment algorithm using clique shows the strength in the search space optimization and partially incomplete image. We make our own database to get the generality. Using the traditional statistical discriminant analysis, 0.05% false acceptance rate (FAR) at 8.83% false rejection rate (FRR) in 1.55 second average matching speed on a Pentium system have been achieved. This makes it possible to construct high performance fingerprint recognition system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.130-137
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• 1992

This paper presents the word recognition method using a neural-fuzzy pattern matching, in order to make a proper speech pattern for a spectrum sequence and to improve a recognition rate. In this method, a frequency variation is reduced by generating binary spectrum patterns through associative memory using a neural network, and a time variation is decreased by measuring the simillarity using a fuzzy pattern matching. For this method using binary spectrum patterns and logic algebraic operations to measure the simillarity, memory capacity and computation requirements are far less than those of DTW using a conventional distortion measure. To show the validity of the recognition performance for this method, word recognition experiments are carried out using 28 DDD city names and compared with DTW and a fuzzy pattern matching. The results show that our presented method is more excellent in the recognition performance than the other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.19-22
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• 2000

This paper presents new on-line handwritten algorithm for continuous alphabet uppercase characters. The algorithm is based on the idea that alphabet uppercase character consists of at most 4 strokes. It tries to determine the maximum output for a recognition result among outputs of four recognizers which have the capacity to discriminate the character using from 1 through 4 stroke information. The recognition module has 4 neural network based recognizers, which can recognize from 1 through 4 stroke character. We also use specialized post-processing techniques for improving the recognition performance. Trained on 440 input data and choosing 390 uppercase words for a recognition test we reached a 92% recognition rate.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.41-44
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• 2002

This paper is related to the enhancement of speech recognition rate using enhanced pronunciation dictionary. Modern large vocabulary, continuous speech recognition systems have pronunciation dictionaries. A pronunciation dictionary provides pronunciation information for each word in the vocabulary in phonemic units, which are modeled in detail by the acoustic models. But in most speech recognition system based on Hidden Markov Model, actual pronunciation variations are disregarded. Without the pronunciation variations in the speech recognition system, the phonetic transcriptions in the dictionary do not match the actual occurrences in the database. In this paper, we proposed the unvoiced rule of semivowel in allophone rules to pronunciation dictionary. Experimental results on speech recognition system give higher performance than existing pronunciation dictionaries.

• Speech Sciences
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• v.12 no.3
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• pp.115-124
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• 2005

We investigated on the submission of multiple candidates in speaker-independent speech recognition by VQ/HMM. Submission of fixed number of multiple candidates has first been examined. As the number of candidates increases by two, three, and four, the recognition error rates were found to decrease by 41%, 58%, and 65%, respectively compared to that of a single candidate. We tried another approach that the candidates within a range of Viterbi scores are submitted. The number of candidates showed geometric increase as the admitted range becomes large. For a practical application, a combination of the above two methods was also studied. We chose the candidates within some range of Viterbi scores and limited the maximum number of candidates submitted to five. Experimental results showed that recognition error rates of less than 10% could be achieved with average number of candidates of 3.2 by this method.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.866-871
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• 2011

In this paper, an improved method of hand detecting and hand gesture recognition is proposed, it can be applied in different illumination condition and complex background. We use Adaptive Skin Threshold (AST) to detect the areas of hand. Then the result of hand detection is used to hand recognition through the improved HMM algorithm. At last, we design a simple program using the result of hand recognition for recognizing "stone, scissors, cloth" these three kinds of hand gesture. Experimental results had proved that the hand and gesture can be detected and recognized with high average recognition rate (92.41%) and better than some other methods such as syntactical analysis, neural based approach by using our approach.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.1
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.16-23
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• 2012

In this paper we discussed a new methodology for Urdu Character Recognition system using Dempster-Shafer theory which can powerfully estimate the similarity ratings between a recognized character and sampling characters in the character database. Recognition of character is done by five probability calculation methods such as (similarity, hamming, linear correlation, cross-correlation, nearest neighbor) with Dempster-Shafer theory of belief functions. The main objective of this paper is to Recognition of Urdu letters and numerals through five similarity and dissimilarity algorithms to find the similarity between the given image and the standard template in the character recognition system. In this paper we develop a method to combine the results of the different distance measurement methods using the Dempster-Shafer theory. This idea enables us to obtain a single precision result. It was observed that the combination of these results ultimately enhanced the success rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.