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Face Recognition based on Weber Symmetrical Local Graph Structure
( Jucheng Yang ),( Lingchao Zhang ),( Yuan Wang ),( Tingting Zhao ),( Wenhui Sun ),( Dong Sun Park ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.4
Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the 5 × 5 neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.
Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine
( Jucheng Yang ),( Yanbin Jiao ),( Naixue Xiong ),( Dongsun Park ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.7
Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.
Jucheng Yang,Naixue Xiong,Vasilakos, A. V.,Zhijun Fang,Dongsun Park,Xianghua Xu,Sook Yoon,Shanjuan Xie,Yong Yang IEEE 2011 IEEE systems journal Vol.5 No.4
<P>In cloud computing communications, information security entails the protection of information elements (e.g., multimedia data), only authorized users are allowed to access the available contents. Fingerprint recognition is one of the popular and effective approaches for priori authorizing the users and protecting the information elements during the communications. However, traditional fingerprint recognition approaches have demerits of easy losing rich information and poor performances due to the complex inputs, such as image rotation, incomplete input image, poor quality image enrollment, and so on. In order to overcome these shortcomings, in this paper, a new fingerprint recognition scheme based on a set of assembled invariant moment (geometric moment and Zernike moment) features to ensure the secure communications is proposed. And the proposed scheme is also based on an effective preprocessing, the extraction of local and global features and a powerful classification tool, thus it is able to handle the various input conditions encountered in the cloud computing communication. The experimental results show that the proposed method has a higher matching accuracy comparing with traditional or individual feature based methods on public databases.</P>
Subsurface Channel Detection Using Color Blending of Seismic Attribute Volumes
Jianhua Cao,Yang Yue,Kunyu Zhang,Jucheng Yang,Xiankun Zhang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
Color is the critical factor in seismic data interpretation and geological targets visualization. And recently, ideas of color blending have brought the enlightenment in attribute combinations for reservoir characterization in petroleum engineering. In this paper, we present this approach of color blending in different color modes and its application in subsurface channel detection by using seismic attributes data. The color models include RGB model, CMY model and HSV model. We firstly calculate sensitive attributes from three dimensional seismic data, including envelop, coherence and spectral decomposition, etc. Then three types of normalized seismic attributes are set as input into the primary color channel of the color models respectively, and then mixed together to create one color blended volume in three dimensional visualization environment. The blended volume has plenty of geological information coming from the three input attributes, resulting in better resolution for channels than the single attribute. Applications in one survey of DQ oilfield show that channels are vividly imaged with special lighted color on the blended volume slices. The spatial distribution characteristics of channels, including the shapes and branches, are clearly depicted. And for the three blending methods, the RGB model is mostly preferred although the CMY model has almost similar performances in channel detection, while HSV model is slightly inferior in this case.
Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure
( Song Dong ),( Jucheng Yang ),( Yarui Chen ),( Chao Wang ),( Xiaoyuan Zhang ),( Dong Sun Park ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.10
Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.
Face Expression Recognition Based on Motion Templates and 4-layer Deep Learning Neural Network
Jianzheng Liu,Xiaojing Wang,Jucheng Yang,Chao Wu,Lijun Liu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
A human facial expression is the formation of facial muscle movement. In our previous research, we proposed a method of identifying facial muscle movement which based on motion templates and GentleBoost. But the method was not robust enough to recognize human expression due to insufficient learning stage. So in this paper, we proposed a new method based on motion templates and 4-layer deep learning neural network to identify human's facial expressions. We recognized Action Unit as a kind of features by using motion templates and adaboost firstly, and then the extracted features were used to feed a 4-layer deep learning neural network to recognize the facial expression. The experimental results have proved that the proposed method can solve the problem encountered in our previous research.