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IEEE 2nd International Conference on Image Information Processing (ICIIP)

作者： Kumar, Ravinder Chandra, Pravin Hanmandlu, Madasu GGSIP Univ Uni Sch Informat & Commun Tech Sect 16C Dwarka Delhi India IIT Dept Elect Engn New Delhi India

ISBN: (纸本)9781467360999;9781467361002

Recently a number of biometric indicators are in use for human identification, but the fingerprint based individual identification is still the dominating biometric indicator. In this paper, we present a fingerprint matching system by exploiting local directional pattern (LDP) based features, which are originally proposed for face recognition and facial expression detection. Fingerprint image texture is encoded by computing the response value of edges in different directions from the extracted region of interest (ROI) images. Single hidden layer feed forward neural network (SLFNN) is trained using three training algorithms namely gradient decent with momentum (GDM), resilient propagation (RP), and scaled conjugate gradient (SCG) to detect the match between test and trainee images. The experimental results show that the RP algorithm converges faster and perform well in terms of matching accuracy as compared to the other two algorithms.

关键词： SLFNN training algorithm local directional pattern Fingerprint Matching Region of Interest (ROI)

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local directional pattern of phase congruency features for illumination invariant face recognition

Local directional pattern of phase congruency features for i...

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Conference on Optical pattern Recognition XXV

作者： Essa, Almabrok E. Asari, Vijayan K. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9781628410310

An illumination-robust face recognition system using local directional pattern (LDP) descriptors in Phase Congruency (PC) space is proposed in this paper. The proposed directional pattern of Phase Congruency (DPPC) is an oriented and multi-scale local descriptor that is able to encode various patterns of face images under different lighting conditions. It is constructed by applying LDP on the oriented PC images. A LDP feature is obtained by computing the edge response values in eight directions at each pixel position and encoding them into an eight bit binary code using the relative strength magnitude of these edge responses. Phase congruency and local directional pattern have been independently used in the field of face and facial expression recognition, since they are robust to illumination changes. When the PC extracts the discontinuities in the image such as edges and corners, the LDP computes the edge response values in different directions and uses these to encode the image texture. The local directional pattern descriptor on the phase congruency image is subjected to principal component analysis (PCA) for dimensionality reduction for fast and effective face recognition application. The performance evaluation of the proposed DPPC algorithm is conducted on several publicly available databases and observed promising recognition rates. Better classification accuracy shows the superiority of the LDP descriptor against other appearance-based feature descriptors such as local Binary pattern (LBP). In other words, our result shows that by using the LDP descriptor the Euclidean distance between reference image and testing images in the same class is much less than that between reference image and testing images from the other classes.

关键词： Face Recognition Phase Congruency local directional pattern Principal Component Analysis

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Robust Facial Expression Recognition Based on local directional pattern

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ETRI JOURNAL 2010年第5期32卷 784-794页

作者： Jabid, Taskeed Kabir, Md. Hasanul Chae, Oksam Kyung Hee Univ Dept Comp Engn Yongin South Korea

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

关键词： Image representation facial expression recognition local directional pattern features extraction principal component analysis support vector machine

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Modified dimensionality reduced local directional pattern for facial analysis

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2018年第3期9卷 725-737页

作者： Ramalingam, S. Perumal Mouli, P. V. S. S. R. Chandra VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

local descriptors play a vital role in face analysis. This paper proposes a modified dimensionality reduced local directional pattern (MDR-LDP) for face analysis that includes both face and facial expression recognition. MDR-LDP is an updated version of dimensionality reduced local directional pattern (DR-LDP) for face recognition. DR-LDP assigns a single code for every region of local directional pattern (LDP) encoded image. It is a compact and efficient code to recognize faces but gives only satisfactory results for facial expression recognition. Every LDP encoded region is reduced to a region and labeled as MDR-LDP. The MDR-LDP descriptor considers the micro structure patterns in the image. The resultant MDR-LDP encoded image is further divided into regions and histograms are generated for each region. The bins of each histogram form the feature vector. The concatenation of all the feature vectors forms the MDR-LDP descriptor and is used for facial expression. The proposed MDR-LDP is robust to noise, illumination changes and pose variations. The experiments were carried out on standard databases and the objective evaluation using the standard metrics prove that MDR-LDP performs superior to the other local descriptors for face analysis.

关键词： local directional pattern Dimensionality reduction Facial expression Feature descriptor Face detection Image classification

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Dimensionality reduced local directional pattern (DR-LDP) for face recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2016年第0期63卷 66-73页

作者： Perumal, Srinivasa R. Mouli, Chandra P. V. S. S. R. VIT Univ Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

local directional pattern (LDP) is a descriptor used for face recognition. It assigns a code for each pixel in the image, and the resultant LDP-encoded image is divided into regions for which each a histogram is generated. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDP, a dimensionality reduced local directional pattern (DR-LDP) is proposed in this paper. The proposed descriptor computes single code for each block by X-ORing the LDP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDP coding pattern constraints. The significance of DR-LDP is the compact code generation for efficient face recognition. The experiments were carried out on standard databases like FERET, extended YALE-B database and ORL. The resultant DR-LDP descriptor provided better recognition rates, outperforming the existing local descriptor-based methods and proving its efficacy. The compact code can be further extended to provide biometric security. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local directional pattern Dimensionality reduction Face recognition Image descriptor Feature descriptor Face descriptor Face detection local patterns

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A new fusion approach for content based image retrieval with color histogram and local directional pattern

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第4期9卷 677-689页

作者： Zhou, Ju-xiang Liu, Xiao-dong Xu, Tian-wei Gan, Jian-hou Liu, Wan-quan Dalian Univ Technol Sch Control Sci & Engn Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Yunnan Normal Univ Key Lab Educ Informalizat Nationalities Minist Educ Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ Postgrad Dept Kunming 650500 Yunnan Peoples R China Curtin Univ Dept Comp Perth WA 6102 Australia

In this paper, we propose a novel color image retrieval approach by using an effective fusion of two types of histograms extracted from color and local directional pattern (LDP), respectively. First, we describe the extraction process of color histogram and LDP. Secondly we present these two features and then develop an effective fusion procedure including feature normalization and a new similarity metric. Thirdly, this new approach is validated after extensive comparisons with several existing state of the art approaches on two benchmark datasets including the Wang's dataset and large size of the Corel-10000 dataset. Finally, a friendly interface for this proposed retrieval system is designed and used to show some retrieval results.

关键词： CBIR Color histogram local directional pattern Distance metrics

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Face Recognition via local directional pattern

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2013年第2期7卷 191-200页

作者： Kim, Dong-Ju Lee, Sang-Heon Sohn, Myoung-Kyu Daegu Gyeongbuk Inst Sci & Technol Div IT Convergence 50-1 Sang Ri Daegu South Korea

In this paper, we propose an illumination-robust face recognition system via local directional pattern images. Usually, local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional pattern descriptor and two-dimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B database and the CMU-PIE database were employed. Through experimental results, the proposed system showed the best recognition accuracy compared to different approaches, and we confirmed the effectiveness of the proposed method under varying lighting conditions.

关键词： Face Recognition local directional pattern

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local directional pattern (LDP) FOR FACE RECOGNITION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第4期8卷 2423-2437页

作者： Jabid, Taskeed Kabir, Md. Hasanul Chae, Oksam Kyung Hee Univ Dept Comp Engn Yongin 446701 Gyeonggi Do South Korea Islamic Univ Technol Dept Comp Sci & Informat Technol Gazipur 1704 Bangladesh

This paper presents a novel local feature descriptor, the local directional pattern (LDP), for recognizing human face. In the proposed feature, different edge responses around each pixel are meticulously encoded to distinguish all kinds of textures available in facial images. The LDP code is also robust enough to provide consistent representation in the presence of random noise and non-monotonic illumination variation. An LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each face is divided into small regions, from where LDP histograms are extracted and concatenated into a single feature vector to efficiently represent the facial image. The recognition is performed by template matching in accordance with CSU face identification evaluation system and evaluated with well studied FERET database. The recognition performance demonstrates the robustness of proposed LDP descriptor for representing appearance of facial image over other existing approaches including local Binary pattern (LBP).

关键词： Facial image representation local directional pattern Texture feature Face recognition local binary pattern

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A comparative Study of local directional pattern for Texture Classification

A comparative Study of Local Directional Pattern for Texture...

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World Symposium on Computer Applications and Research (WSCAR)

作者： Shabat, Abuobayda M. Tapamo, Jules R. Univ KwaZulu Natal Sch Engn Durban South Africa

ISBN: (纸本)9781479928064

Texture is an important feature for image analysis in which each pixel is classified based on its neighborhood. It is used for surface characterization in many applications, such as medical imaging, remote sensing and quality control. The purpose of this paper is to investigate the performance for the newly purposed local directional pattern (LDP) and compared to the popular Gray level co-occurrence matrix (GLCM). In this paper, texture classification power of two feature methods, Gray Level Co-occurrence Matrix (GLCM) and local directional pattern(LDP) are compared. Experiments are conducted on 25 Texture types selected from Brodatz album. Classification are carried out using 4 different classifiers (Naive-bayes(NB), Multilayer Perceptron(MLP), Support Vector Machine (SVM), k-nearest Neighbor Algorithm(k-NN)) in different conditions. In this study it is established that the LDP has the best accuracy at 97% using Multilayer Perceptron and 96% using SVM, compared to GLCM. In the literature local directional pattern (LDP) has mainly been used to extract features in biometrics applications. In this paper LDP is used to characterize general purpose textures. It is shown that outperforms the very popular Gray Level Co-occurrence Matrix and Haralick features.

关键词： component Texture features Gray Level Co-occurrence Matrix local directional pattern Classification

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Optimized local directional pattern for Robust Facial Expression Recognition

Optimized Local Directional Pattern for Robust Facial Expres...

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IEEE/OSA/IAPR International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Rahman, Arifur Ali, Liakot Bangladesh Univ Engn & Technol Inst Informat & Commun Technol Dhaka Bangladesh

ISBN: (纸本)9781467311526

A novel low-cost highly discriminatory feature space is introduced for facial expression recognition, which incorporates weight with the Optimized local directional pattern (OLDP), capable of robust performance over a range of image resolutions. In addition, we use Adaboost to pick a small set of high-flying features, which are used by the Support Vector Machine (SVM) to classify facial expressions proficiently. Experimental results show that the proposed technique improves both the accuracy and the speed of the final classifier compares to other existing state-of-the-art methods.

关键词： local directional pattern expression recognition feature extraction support vector machine adaboost

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