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نتیجه جستجو - Face recognition

تعداد مقالات یافته شده: 13
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1 Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition-2019
Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs have been made for traditional face recognition by applying deep learning on a huge amount of labeled VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due to large domain difference as well as insufficient pairwise images in different modalities during training. In general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss of useful face information, resulting in a decrease in the performance of HFR problem. It is important to eliminate modal-related information and retain more facial identity information. In this paper, we propose a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both interand intra-class information for effectively training the deep model. To make full use of the various layers of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network for joint supervision training, which enables multiple layers of the network to obtain discriminative identity features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Keywords: Heterogeneous face recognition | Deep learning | Joint supervision loss | Feature fusion
مقاله انگلیسی
2 A survey on deep learning based face recognition
مروری بر شناخت چهره مبتنی بر یادگیری عمیق-2019
Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed. This paper summarizes about 330 contributions in this area. It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching. A summary of databases used for deep face recognition is given as well. Finally, some open challenges and directions are discussed for future research.
Keywords: Deep learning | Face recognition | Artificial Neural Network | Convolutional Neural Networks | Autoencoder | Generative Adversarial Networks
مقاله انگلیسی
3 Sparse deep feature learning for facial expression recognition
یادگیری ویژگی های عمیق پراکنده برای تشخیص چهره صورت-2019
While weight sparseness-based regularization has been used to learn better deep features for image recognition problems, it introduced a large number of variables for optimization and can easily con- verge to a local optimum. The L2-norm regularization proposed for face recognition reduces the impact of the noisy information, while expression information is also suppressed during the regularization. A feature sparseness-based regularization that learns deep features with better generalization capability is proposed in this paper. The regularization is integrated into the loss function and optimized with a deep metric learning framework. Through a toy example, it is showed that a simple network with the proposed sparseness outperforms the one with the L2-norm regularization. Furthermore, the proposed approach achieved competitive performances on four publicly available datasets, i.e., FER2013, CK + , Oulu-CASIA and MMI. The state-of-the-art cross-database performances also justify the generalization capability of the proposed approach.
Keywords: Expression recognition | Feature sparseness | Deep metric learning | Fine tuning | Generalization capability
مقاله انگلیسی
4 Simultaneous learning of reduced prototypes and local metric for image set classification
یادگیری همزمان از نمونه های اولیه کاهش یافته و متریک محلی برای طبقه بندی مجموعه تصویر-2019
Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifi- cally, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classifi- cation error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label “closer”to each other, while pushing dissimilar ones “far away”. Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on im- age set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications.
Keywords: Image set classification | Prototype learning | Metric learning | Face recognition | Expert system
مقاله انگلیسی
5 Dependence structure of Gabor wavelets based on copula for face recognition
ساختار وابستگی Gabor wavelets بر اساس کوپول برای تشخیص چهره-2019
Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet sub- bands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion.
Keywords: Face recognition | Gabor wavelets | Gaussian copula | Covariance matrix | Particle swarm optimization
مقاله انگلیسی
6 General and specific factors in the processing of faces
عوامل عمومی و خاص در پردازش چهره-2017
The ability to recognize faces varies considerably between individuals, but does performance co-vary for tests of different aspects of face processing? For 397 participants (of whom the majority were university students) we obtained scores on the Mooney Face Test, Glasgow Face Matching Test (GFMT), Cambridge Face Memory Test (CFMT) and Composite Face Test. Overall performance was significantly correlated for each pair of tests, and we suggest the term f for the factor underlying this pattern of positive correlations. However, there were large variations in the amount of variance shared by individual tests: The GFMT and CFMT are strongly related, whereas the GFMT and the Mooney test tap largely independent abilities. We do not replicate a frequently reported relationship between holistic processing (from the Composite test) and face recognition (from the CFMT)—indeed, holistic processing does not correlate with any of our tests. We report associations of performance with digit ratio and autism-spectrum quotient (AQ), and from our genome-wide association study we include a list of suggestive genetic associations with perfor mance on the four face tests, as well as with f.
Keywords: Face perception | Face recognition | f | Holistic processing | Individual differences | Autism-spectrum quotient (AQ) | Genome-wide association study (GWAS)
مقاله انگلیسی
7 Computation partitioning for mobile cloud computing in a big data environment
پارتیشن بندی محاسبات برای محاسبات ابری سیار در یک محیط داده های بزرگ-2017
The growth of mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment that are available to mobile clients while addressing the dynamic changes in network bandwidth. Big data can be handled via MCC. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve performance. First, we constructed a model of stateful data streaming and investigated the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single frame data flow, task scheduling and executing efficiency. We also defined the problem for a multi-frame data flow calculation segmentation decision that is optimized for dynamic conditions and provided an analysis. Second, we proposed a computation partitioning method for single frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. We followed the scheduling method to provide the optimal calculation for data frame execution time after computation partitioning and the best computation partitioning method. Third, we explored a calculation segmentation method for single frame data flow based on multi-frame data using multi-frame data optimization adjustment and prediction of future changes in network bandwidth. We were able to demonstrate that the calculation method for multi-frame data in a changing network bandwidth environment is more efficient than the calculation method with the limitation of calculations for single frame data. Finally, our research verified the effectiveness of single frame data in the application of the data stream and analyzed the performance of the method to optimize the adjustment of multi-frame data. We used a mobile cloud computing platform prototype system for face recognition to verify the effectiveness of the method.
Index Terms: Big data | computation partitioning | data stream | dynamic environment | mobile cloud computing | stateful
مقاله انگلیسی
8 Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reduction
تحلیل آزاد و فازی تفکیک کننده طول با چارچوب هیپربولیک کسینوسی برای کاهش بعدی بودن-2017
Dimensionality Reduction (DR) is very useful and popular in many application areas of expert and in telligent systems, such as machine learning, finance, data and text mining, multimedia mining, image processing, anomaly detection, defense applications, bioinformatics and natural language processing. DR is widely applied for better data visualization and improving learning in all the above fields. In this manuscript, we propose a novel DR approach namely, Noisy-free Length Discriminant Analysis (NLDA) by developing Noisy-free Relevant Pattern Selection (NRPS). Traditional pattern selection methods discrim inate boundary and non-boundary patterns with the help of class information and nearest neighbors. And these methods completely ignore noisy patterns which may degrade the performance of subsequent subspace learning. To overcome this, we develop Noisy-free Relevant Pattern Selection (NRPS), in which data instances are partitioned into boundary, non-boundary and noisy patterns. With the help of noisy free boundary and non-boundary patterns, Noisy-free Length Discriminant Analysis (NLDA) has been pro posed by developing new within and between-class scatters. These scatters model discriminations be tween lengths (L2-norms) of different class instances by considering only boundary and non-boundary patterns, while ignoring noisy patterns. A cosine hyperbolic frame work has been developed to formulate the objective of NLDA. Moreover, NLDA can also model the discrimination of multimodal data as different class data may consist of different lengths. Experimental study conducted on the synthesized data, UCI, and leeds butterfly databases. Moreover, an experimental study over human and computer interaction, i.e., face recognition (one of the application areas of expert and intelligent systems), has been performed. And, these studies prove that the proposed method can produce better discriminated subspace compare to the state-of-the-art methods.
Keywords: Dimensionality reduction | Subspace learning | Discriminant analysis | Relevant patterns | Cosine hyperbolic | Face recognition
مقاله انگلیسی
9 بهره برداری از فن آوری اینترنت اشیاء برای بهبود بهداشت خانه های هوشمند از طریق شناسایی بیمار و تشخیص احساسات
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 43
در حال حاضر، تعداد رو به افزایش بیمارانی که در خانه تحت درمان هستند به طور عمده در کشورهایی مانند ژاپن، ایالات متحده آمریکا و اروپا است. همچنین، تعداد افراد مسن به طور چشمگیری در پانزده سال گذشته افزایش پیدا کرده و این افراد اغلب در خانه تحت درمان قرار می گیرند و گاهی اوقات وارد شرایط بحرانی می شوند که ممکن است مستلزم کمک باشد (برای مثال، تصادف یا افسرگی). پیشرفت در محاسبات فراگیر و اینترنت اشیا (IoT) تجهیزات کارآمد و ارزانی فراهم کرده که شامل ارتباطات بی سیم و دوربین ها، مانند تلفن های هوشمند و یا دستگاه های جاسازی شده مانند Raspberry Pi می باشد. محاسبات جاسازی شده به کارگیری خانه های بهداشت هوشمند (HSH) را امکان پذیر می کند که می تواند موجب بهبود درمان پزشکی در منزل شود. استفاده از دوربین و پردازش تصویر در اینترنت اشیا هنوز هم به طور کامل در ادبیات مورد بررسی قرار نگرفته، به ویژه در زمینه ی HSH. اگر چه استفاده از تصاویر به طور گسترده ای برای رسیدگی به مسائلی از قبیل ایمنی و نظارت در خانه مورد استفاده قرار گرفته، برای کمک به بیماران و / یا افراد مسن به عنوان بخشی از سیستم مراقبت در خانه کمتر به کار گرفته شده است. به نظر ما، این تصاویر می تواند به پرستاران یا مراقبان برای رسیدگی به بیماران نیازمند کمک به موقع کمک کرده و اجرای آن توسط فن آوری های اینترنت اشیا می تواند بسیار آسان و ارزان باشد. این مقاله به بررسی استفاده از تصاویر بیمار و تشخیص عاطفی برای کمک به بیماران و افراد مسن در زمینه مراقبت های بهداشتی در خانه می پردازد. همچنین ادبیات موجود را مورد بحث قرار داده و نشان می دهیم که بیشتر مطالعات انجام شده در این زمینه از تصاویر به منظور نظارت بر بیماران استفاده نمی کنند. علاوه بر این، مطالعات اندکی وجود دارند که حالت روحی بیمار را در نظر گرفته باشند که برای بازیابی آنها از بیماری بسیار مهم است. در نهایت، نمونه ی خود را طراحی می کنیم که بر اساس سیستم های عامل متعدد برای محاسبات بوده و نتایجی را ارائه می کنیم که نشان دهنده ی عملی بودن رویکرد ما هستند.
کلمات کلیدی: خانه های هوشمند | سلامت الکترونیک | اینترنت اشیاء | تشخیص چهره | تشخیص احساسات
مقاله ترجمه شده
10 تشخیص چهره بر اساس Kinect
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 34
در این مقاله، الگوریتم جدیدی ارائه می دهیم که از داده های کم کیفیت قرمز، سبز، آبی و عمق (RGB-D) حاصل از حسگر Kinect برای تشخیص چهره تحت شرایط چالش برانگیز استفاده می کند. این الگوریتم چندین مشخصه استخراج کرده و آنها را در سطح مشخصه تلفیق می کند. روش تلفیق مشخصۀ بهتر که اطلاعات اضافی را حذف کرده و فقط مشخصات مهم را برای حداکثر تفکیک کلاسی ممکن حفظ می کند، توسعه داده شده است. همچنین پایگاه دادۀ چهرۀ 3D جدیدی را که از حسگر Kinect به دست آمده و برای جامعۀ پژوهشی عرضه شده است، ارئه می کنیم. این پایگاه داده شامل بیش از 5000 تصویر چهره (RGB-D) از 52 نفر با ژست ها، حالات، شدت نور و پوشش های مختلف می باشد. بر اساس سه تغییر اول و فقط با استفاده از داده های نویزی عمق، الگوریتم پیشنهادی می تواند به نرخ تشخیص 72.5% برسد که به طور قابل توجهی بیشتر از 41.9% است که روش مبنای LDA به آن رسیده است. تحت تغییرات شدت نور، ژست و حالت و با ترکیب با اطلاعات الگو، نرخ تشخیص 91.3% به دست آمده است. این نتایج به امکان استفاده از حسگرهای 3D ارزان برای تشخیص چهرۀ بلادرنگ اشاره می کند.
کلمات کلیدی: تشخیص چهره | حسگر Kinect، تصاویر چهرۀ سه بعدی | مشخصۀ Gabor | LDA
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