• Journal of Multimedia Information System
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• v.6 no.1
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• pp.15-22
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• 2019

There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1718-1721
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• 1997

An image processing algorithm is developed in order to recognize the type of cars, the position of a number plate and the characters on the plate. to recognize the type of cars, comparison of two images is used. One has a car image, the other is just a background image without car. After that recognition, a vertical line filter is used to find the location of the plate. Finally the simularity mehod is used to recognize the numbers on plates.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.305-309
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• 2006

In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory(ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.581-584
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• 1997

Discussed is stroke identification technique for automatic recognition of kanji characters without using the order of drawing strokes of a character.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.936-940
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• 1987

This paper describes the recognition boundary of Hangeul character to interpret variance factor in accordance with various combination. When the recognition algorithm based on comparing the MSE value with the one of the standard pattern in $16{\times}16$ images is performed, we come to a conclusion that we have towe must make a decision MSE value above 34 in order to achive theve recognition rate larger than 90%. Also we understand that varing component coordinates method based on statistical process of each character pattern is preferred.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.586-591
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• 2008

This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.91-94
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• 2005

The performance of speech recognition is degraded by the mismatch between training and test environments. Many methods have been presented to compensate for additive noise and channel effect in the cepstral domain, and Cepstral Mean Subtraction (CMS) is the representative method among them. Recently, high order cepstral moment normalization method has introduced to improve recognition accuracy. In this paper, we apply high order moment normalization method and smoothing filter for real-time processing. In experiments using Aurora2 DB, we obtained error rate reduction of 49.7% with the proposed algorithm in comparison with baseline system.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.50-59
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• 1994

This paper presents a speech recognition method utilizing third-order cumulants as a feature vector and a neural network for recognition. The use of higher-order cumulants provides desirable uncoupling between the gaussian noise and speech, which enables us to estimate the coefficients of AR model without bias. Unlike the conventional method using second-order statistics, the proposed one exhibits low bias even in SNR as low as 0 dB at the expense of higher variance. It is confirmed through computer simulation that recognition rate of korean single-vowels with the cumulant-based method is much higher than the results with the conventional method even in low SNR.