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FISH-CC: novel face identification using spider hierarchy (FISH) with a classic classifier

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第4期18卷 3925-3941页

作者： Ranganathan, Bhuvaneshwari Palanisamy, Geetha Anna Univ Coll Engn Guindy Dept Comp Sci & Engn Chennai Tamil Nadu India

Face is one of the most important biometric traits utilized by humans for recognition. Face recognition is the prominent biometric method for human authentication, and it is used in several domains due to its unique features, non-intrusive, and convenience compared to other biometric systems like fingerprint or palmprint scans. Although the field of face recognition has advanced significantly, there are still problems that prevent accuracy from surpassing that of humans. This study proposes a novel and effective framework, named Face Identification utilizing Spider Hierarchy with a Classic Classifier (FISH-CC), aimed at recognizing a person's face, gender, and age. This framework incorporates a novel face boundary localization scheme based on cooperative game theory (CGT), enhancing facial detection performance by accurately detecting facial contour. Features are extracted from the detected faces using a modified local binary pattern (mLBP). To optimize feature selection, a CGT-based algorithm, known as the extended contribution selection algorithm (ECSA) with forward feature selection (FFS), is implemented. Finally, Spider Hierarchy (SH) integrated with a Classic Classifier (CC) is used for face identification. To assess the effectiveness of the proposed method, a number of tests are carried out, and the labeled faces in the wild (LFW) database are utilized to validate the performance. The outcomes of this study demonstrated that the proposed FISH-CC achieves a superior accuracy rate of 99.60% when compared to the existing approaches.

关键词： Face recognition Game theory Single sample per person Spider Hierarchy Classic Classifier modified local binary pattern

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Toward a pedestrian detection method by various feature combinations

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2019年第3期23卷 191 - 201页

作者： Abari, Mina Etehadi Naghsh-Nilchi, Ahmadreza Univ Isfahan Esfahan Iran

Pedestrian detection has been a crucial issue over the last decades. The existing pedestrian detection methods are still face abrupt illumination, partial occlusion, different poses of humans, and cluttered backgrounds challenges. Consequently, the significance of pedestrian detection systems encourages us to propose a new method to address some of these challenges and offer higher accuracy rate. Noting that the power of various kinds of features are different and a single type of feature cannot extract the comprehensive information of human shape. Taking this fact into consideration, we combined pragmatic and useful features in order to detect pedestrian more accurate. Indeed, we combine histogram of oriented gradients (HOG), a proposed modified local binary pattern (M-LBP), and a proposed modified Haar-like features (M-Haar) to achieve these goals. By applying the proposed method, it is possible to extract various information on human shapes including the edge information, texture information, and local shape information. After feature extraction, Cascade Adaboost classifier is used to detect pedestrian images from nonpedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are applied. The extensive experimental results demonstrate that our approach outperforms the traditional methods in terms of the accuracy and robustness.

关键词： Pedestrian detection modified local binary pattern M-LBP histogram of oriented gradients HOG modified Haar-like features Adaboost classifier

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modified local binary pattern (MLBP) FOR ROBUST FACE RECOGNITION

MODIFIED LOCAL BINARY PATTERN (MLBP) FOR ROBUST FACE RECOGNI...

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International Conference on Neural Computation Theory and Applications

作者： Islam, Mohammad Moinul Asari, Vijayan K. Islam, Mohammed Nazrul Karim, Mohammad A. Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA Farmingdale SUNY Dept Secur Syst Farmingdale NY 11735 USA

ISBN: (纸本)9789898425843

This paper presents an improvement of local binary pattern (LBP) for robust face representation under varying lighting conditions. Original LBP operator compares pixels in a local neighbourhood with the centre pixel and converts the resultant binary string to 8-bit integer value. So, it is less effective under difficult lighting conditions where variation between pixels is negligible. Our proposed MLBP uses two stage encoding procedure which is more robust in detecting this variation in a local patch. The performance of the proposed method is compared with the baseline LBP under different illumination conditions.

关键词： local binary patterns Object recognition Illumination invariance modified local binary pattern

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A Model of local binary pattern Feature Descriptor for Valence Facial Expression Classification 14

A Model of Local Binary Pattern Feature Descriptor for Valen...

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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Agada, Ruth Yan, Jie Bowie State Univ Dept Comp Sci Bowie MD 20715 USA

ISBN: (纸本)9781509002870

Recognition of spontaneous emotion would significantly influence human-computer interaction and emotion-related studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.

关键词： local binary pattern modified local binary pattern feature extraction edge detection support vector machine

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A Model of local binary pattern Feature Descriptor for Valence Facial Expression Classification

A Model of Local Binary Pattern Feature Descriptor for Valen...

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IEEE International Conference on Machine Learning and Applications

作者： Ruth Agada Jie Yan Department of Computer Science Bowie State University Bowie U.S.A

ISBN: (纸本)9781509002887

Recognition of spontaneous emotion would significantly influence human-computer interaction and emotionrelated studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.

关键词： local binary pattern modified local binary pattern feature extraction edge detection support vector machine

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Mutibiometric System Based on Game Theory 12th

Mutibiometric System Based on Game Theory

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12th International Conference on Image Analysis and Recognition (ICIAR)

作者： Aljohani, Nawaf Ahmad, Foysal Roy, Kaushik Shelton, Joseph N Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9783319208015;9783319208008

Biometric systems based on a single biometric trait have drawbacks that are alleviated by multibiometric systems, which combine multiple sources of information. The novelty of this research effort is that Coalition Game Theory (CGT) is applied to improve the performance of the iris and face based multibiometric system. The CGT technique selects the most salient patches obtained using the local binary patterns (LBP) and modified LBP (mLBP) feature extraction techniques. The CGT chooses patches that have better individual importance along with a strong interaction with other patches based on the Shapely value. Results show that CGT model maintains impressive recognition accuracy while using smaller image areas for recognition. More specifically, CGT outperforms the LBP and mLBP techniques.

关键词： Multibiometric system Coalition game theory modified local binary pattern Patch selection

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Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

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COMPUTER JOURNAL 2014年第9期57卷 1383-1394页

作者： Varghese, Abraham Balakrishnan, Kannan Varghese, Reji Rajan Paul, Joseph S. Adi Shankara Inst Engn & Technol Kalady Ernakulam India CUSAT Dept Comp Applicat Cochin Kerala India Cooperat Med Coll Cochin Kerala India Indian Inst Informat Technol & Management Kerala Trivandrum Kerala India

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variance-based local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MOD-LTP and iteratively reweighting the moment features of MOD-LTP based on the user's feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R. S. (2010) local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897-903.) in retrieving the first 10 relevant images.

关键词： content-based image retrieval local binary pattern modified local binary pattern modified local ternary pattern rotational scaling & translational invariant features

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Multispectral Iris Recognition Using Patch Based Game Theory 11

Multispectral Iris Recognition Using Patch Based Game Theory

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11th International Conference on Image Analysis and Recognition (ICIAR)

作者： Ahmad, Foysal Roy, Kaushik Popplewell, Khary N Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9783319117553;9783319117546

Multispectral imaging offers potential to improve the recognition performance of an iris biometric system. The novelty of this research effort is that a Coalition Game Theory (CGT) is proposed to select only the important patches that are obtained using the modified local binary pattern (mLBP) operator. The mLBP fuses both the sign and magnitude difference vector in an effort to extract feature from normalized iris images. The CGT selects patches based on the Shapely value that have better individual importance along with a strong interaction with other patches to improve the overall performance. Results show that CGT model maintains better recognition accuracy while reducing the overall surface area needed for recognition purpose.

关键词： Multispectral iris recognition Coalition game theory modified local binary pattern Patch selection

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Multispectral Iris Recognition Utilizing Hough Transform and modified LBP

Multispectral Iris Recognition Utilizing Hough Transform and...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Popplewell, Khary Roy, Kaushik Ahmad, Foysal Shelton, Joseph N Carolina Agr & Tech State Univ Ctr Adv Studies Ident Sci Greensboro NC 27411 USA

ISBN: (纸本)9781479938407

This paper presents a multispectral iris recognition scheme using Circular Hough Transform (CHT) and a modified local binary pattern (mLBP) feature extraction technique. The CHT is used to localize the iris regions from the multispectral iris images. We also apply the binary thresholding and edge detection techniques in an effort to reduce the effects of over and under segmentation in multispectral iris images in which iris and pupil boundaries are not clearly separable. Furthermore, we apply mLBP in an attempt to elicit the iris feature elements. The mLBP technique combines both the sign and magnitude features for the improvement of iris texture classification performance. The identification and verification performance of the proposed scheme is validated using a multispectral iris dataset of 3120 images.

关键词： Multispectral iris recognition modified local binary pattern circular Hough transform

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Multibiometric System Using Level Set, modified LBP and Random Forest

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2014年第3期14卷 1450013-1450013页

作者： Roy, Kaushik O'Connor, Brian Ahmad, Foysal Kamel, Mohamed S. North Carolina A & T State Univ Dept Comp Sci Greensboro NC 27401 USA Univ Waterloo Ctr Pattern Anal & Machine Intelligence Waterloo ON Canada

Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibio-metric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris-face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.

关键词： Multibiometrics distance regularized level-set iris segmentation feature extraction modified local binary pattern feature subset selection random forest

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