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Driver fatigue recognition based on facial expression analysis using local binary patterns

OPTIK 2015年第23期126卷 4501-4505页

作者： Zhang, Yan Hua, Caijian Sichuan Univ Sci & Engn Sch Comp Sci Dept Software Engn Chengdu Peoples R China

Driver fatigue is a major cause of traffic accidents. Automatic vision-based driver fatigue recognition is one of the most prospective commercial applications based on facial expression analysis technology. Deriving an effective face location from original driver face images is a vital step for successful fatigue facial expression recognition. In this paper, we empirically adopt fast and robust face detection algorithm to describe and normalizing facial expression images. We evaluate facial representation based on statistical local features, local binary patterns, for person-independent fatigue facial expression recognition, and observe that LBP features perform stably and robustly over a useful range of fatigue face images. Moreover, we adopt AdaBoost to learn the most discriminative fatigue facial LBP features from a large LBP feature pool, which is a critical problem but seldom addressed in the existing work. We observe in our experiments that Boost-LBP features perform stably and robustly, and best recognition performance is obtained by using SVM with Boost-LBP features. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Driver fatigue detection Facial expression recognition local binary patterns Adaboost Support vector machine

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Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

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MAGNETIC RESONANCE IMAGING 2011年第2期29卷 230-242页

作者： Zhou, Zhenyu Liu, Wei Cui, Jiali Wang, Xunheng Arias, Diana Wen, Ying Bansal, Ravi Hao, Xuejun Wang, Zhishun Peterson, Bradley S. Xu, Dongrong Columbia Univ Dept Psychiat Pediat Brain Imaging Lab MRI Unit New York NY 10032 USA New York State Psychiat Inst & Hosp Pediat Brain Imaging Lab New York NY 10032 USA Minist Educ Key Lab Brain Funct Genom Shanghai Peoples R China E China Normal Univ Shanghai Key Lab Brain Funct Gen Shanghai 20062 Peoples R China E China Normal Univ Shanghai Key Lab Magnet Resonance Shanghai 20062 Peoples R China

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstra

关键词： DWI Tensor estimation local binary patterns 2D partial least square Outlier Infant

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Ear recognition using local binary patterns: A comparative experimental study

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 118卷 182-200页

作者： Hassaballah, M. Alshazly, Hammam A. Ali, Abdelmgeid A. South Valley Univ Comp Sci Dept Fac Comp & Informat Luxor Egypt South Valley Univ Math Dept Fac Sci Qena 83523 Egypt Minia Univ Comp Sci Dept Fac Comp & Informat Al Minya 61519 Egypt

Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions;namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%;while, their performance faces difficulties when the level of distortions

关键词： Intelligent biometrics systems Identity verification Ear recognition local binary patterns Experimental evaluation

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A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

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EVOLVING SYSTEMS 2021年第2期12卷 503-517页

作者： Gani, Gulnawaz Qadir, Fasel Univ Kashmir Dept Comp Sci North Campus Baramulla 193103 Jammu & Kashmir India

Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to local binary patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.

关键词： Copy-Move Forgery Cellular Automata Passive method Thresholding local binary patterns

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A comparative study on facial expression recognition using local binary patterns, convolutional neural network and frequency neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 24369-24385页

作者： Kumar, Sanjeev Sagar, Vikas Punetha, Deepak GL Bajaj Inst Technol & Management Dept Master Comp Applicat Greater Noida Uttar Pradesh India Noida Inst Engn & Technol Dept Artificial Intelligence Greater Noida Uttar Pradesh India Vellore Inst Technol VIT Univ Sch Elect Engn Chennai 600127 Tamilnadu India

As the principal processing method for nonverbal intentions, Facial Expression Recognition (FER) is an important and promising topic of computer vision and artificial intelligence, as well as one of the subject areas of symmetry. This research work provides a thorough and well-organized comprehensive comparative empirical study of facial expression recognition based on a deep learning study in frequency domain, convolution neural network, and local binary patterns features. We have attained the FER by incorporating neutral, joy, anger, fear, sadness, disgust, and surprise as seven universal emotional categories. In terms of methodology, we present a broad framework for a traditional FER approach and analyze the possible technologies that can be used in each component to emphasis the contrasts and similarities. Even though there has been a lot of research done with static images, there is still a lot of work being done to develop new ways that are easier to compute and use less memory than prior methods. This research could pave the way for a new approach to facial emotion identification in terms of accuracy and high-performance.

关键词： Facial expression recognition local binary patterns Convolutional neural network Frequency neural network Feature extraction Deep learning

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Facial Expression Recognition Based on local binary patterns and Kernel Discriminant Isomap

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SENSORS 2011年第10期11卷 9573-9588页

作者： Zhao, Xiaoming Zhang, Shiqing Taizhou Univ Dept Comp Sci Taizhou 317000 Peoples R China Taizhou Univ Sch Phys & Elect Engn Taizhou 318000 Peoples R China

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap).

关键词： kernel isometric mapping dimensionality reduction local binary patterns facial expression recognition

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Learning local binary patterns for gender classification on real-world face images

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PATTERN RECOGNITION LETTERS 2012年第4期33卷 431-437页

作者： Shan, Caifeng Philips Res Eindhoven Netherlands

Gender recognition is one of fundamental face analysis tasks. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). local binary patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.81% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Gender classification local binary patterns Face image analysis

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A simple method for improving local binary patterns by considering non-uniform patterns

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PATTERN RECOGNITION 2012年第10期45卷 3844-3852页

作者： Nanni, Loris Brahnam, Sheryl Lumini, Alessandra Univ Padua Dept Informat Engn Padua Italy Missouri State Univ Springfield MO 65804 USA Univ Bologna DEIS I-47521 Cesena Italy

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Texture descriptors local binary patterns local ternary patterns Non-uniform patterns Support vector machines

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Hyperdimensional Computing With local binary patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第2期67卷 601-613页

作者： Burrello, Alessio Schindler, Kaspar Benini, Luca Rahimi, Abbas Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Univ Bern Univ Hosp Bern Inselspital Sleep Wake Epilepsy CtrDept Neurol Bern Switzerland

Objective: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Methods: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. Results: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74x lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a p-value < 0.01) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Conclusion and significance: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations;2) one-shot learning from seizure examples;3) linear computational scalability for increasing number of electrodes;and 4) generation of transparent codes that enables post-translational support for clinical decision m

关键词： iEEG one-shot learning local binary patterns symbolic dynamics hyperdimensional computing seizure detection localization of seizure onset zone

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Data-driven techniques for smoothing histograms of local binary patterns

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PATTERN RECOGNITION 2016年 60卷 734-747页

作者： Ylioinas, Juha Poh, Norman Holappa, Jukka Pietikainen, Matti Univ Oulu Fac Informat Technol & Elect Engn Ctr Machine Vis & Signal Anal POB 4500 FI-90014 Oulu Finland Univ Surrey Fac Engn & Phys Sci Dept Comp Guildford GU2 7XH Surrey England

local binary pattern histograms have proved very successful texture descriptors. Despite this success, the description procedure bears some drawbacks that are still lacking solutions in the literature. One of the problems arises when the number of extractable local patterns reduces while their dimension increases rendering the output histogram descriptions sparse and unstable, finally showing up as a reduced recognition rate. A smoothing method based on kernel density estimation was recently proposed as a means to tackle the aforementioned problem. A constituent part of the method is to determine how much to smooth a histogram. Previously, this was solved via trial-and-error in a problem-specific manner. In this paper, the goal is to present data-driven methods to determine this smoothing automatically. In the end, we present unsupervised and supervised methods for the given task and validate their performance with a representative set of local binary pattern variants in texture analysis problems covering material categorization and face recognition. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local binary patterns local binarized descriptors Histogram Soft-assignment Kernel density estimation Histogram smoothing

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