• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.45-48
• /
• 2002

In this paper, we propose an iris recognition system using Homogeneous Texture descriptor of MPEG-7 standard. The texture of iris is generally used in iris recognition system. We segment the pupil with Hough transform and the boundary of iris with it's gray level difference between the white of the eye. To extract Homogeneous Texture descriptor, this iris image is transformed into polar coordinates. The extracted descriptor is then compared with the reference in DB. If their distance is larger than threshold, they are recognized as different iris. Test results will show that Homogeneous Texture descriptor can be a good measure for iris recognition system.

• The KIPS Transactions:PartB
• /
• v.11B no.4
• /
• pp.421-428
• /
• 2004

There are three texture descriptors in MPEG-7 : Homogeneous Texture, Edge Histogram and Texture Browsing. In this paper, a comparative analysis is presented on the capability of MPEG-7 texture descriptors for iris recognition as part of an MPEG-7 application using descriptors. Through the experiments of comparing the clustering efficiency and error distribution of the descriptors using 560 iris images, their discriminating capabilities for different iris groups are analyzed. The results show that Homogenous Texture descriptor is the best discriminator among three descriptors to recognize the iris pattern. However, compared with the conventional iris recognition methods, it needs more efforts to enhance the results.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.402-410
• /
• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.327-336
• /
• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.4
• /
• pp.129-137
• /
• 1994

In this paper, a new texture feature extraction method for texture image classification is proposed. The proposed method is a modified texture spectrum method. It uses local weighting averaged texture unit, that is, the neighbor pixels are weithted and averaged in 4-direction and the calculated values are compared with center pixel to find texture units. The proposed method has only 81 texture units and these units are really good features for texture classification. The proposed method is applied to vegetable images and Blodatz album images and compared with several conventional methods for the feature extraction time and the recognition rate.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.2
• /
• pp.144-150
• /
• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• The KIPS Transactions:PartB
• /
• v.12B no.3 s.99
• /
• pp.257-264
• /
• 2005

The number of vehicles are rapidly increased as our society is developed. The vehicle recognition has been studied for a while because many people acknowledged it has critical functions to solve the problems of traffic control or vehicle-related crimes. In this paper a novel method is proposed to recognize vehicle models corresponding makers. Vehicles' models are recognized based on the texture parameters from segmented radiator region above a number plate. A three-layer neural network was built and trained with the texture features for recognition. The proposed method shows $93.7\%$ of recognition rate and $99.7\%$ of specificity for vehicles' model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.4
• /
• pp.316-320
• /
• 2016

In this paper, we presents a pattern recognition by considering the spatial co-occurrence among micro-patterns of texture images. The micro-patterns of texture image have been extracted by local binary pattern based on median(MLBP) of block image, and the recognition process is based on co-occurrence among MLBPs. The MLBP is applied not only to consider the local character but also analyze the pattern in order to be robust noise, and spatial co-occurrence is also applied to improve the recognition performance by considering the global space of image. The proposed method has been applied to recognized 17 RGB images of 120*120 pixels from Mayang texture image based on Euclidean distance. The experimental results show that the proposed method has a texture recognition performance.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.437-443
• /
• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• Journal of Information Processing Systems
• /
• v.14 no.1
• /
• pp.191-204
• /
• 2018

To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.