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检索条件"主题词=Face Recognition Algorithm"

共 55 条记录，以下是41-50 订阅

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face recognition in real-world images

Face recognition in real-world images

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xavier Fontaine Radhakrishna Achanta Sabine Susstrunk School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781509041183

face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training examples and is computationally efficient. Our method consists of performing a novel alignment process followed by classification using sparse representation techniques. We present our recognition rates on a difficult dataset that represents real-world faces where we significantly outperform state-of-the-art methods.

关键词： face recognition sparse representation alignment mesh warping facial landmarks Facial recognition sparse representation images Alignment face recognition algorithm face recognition system

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Preliminary Studies on the Good, the Bad, and the Ugly face recognition Challenge Problem

Preliminary Studies on the Good, the Bad, and the Ugly Face ...

引用

IEEE Computer Society Conference on Computer Vision and Pattern recognition Workshops

作者： Yui Man Lui David Bolme P. Jonathon Phillips J. Ross Beveridge Bruce A. Draper Department of Computer Science Colorado State University Fort Collins CO 80523 USA The National Institute of Standards and Technology Gaithersburg MD 20899 USA

ISBN: (纸本)9781467316118

face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performance envelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challenge problem has been introduced to focus on hard aspects of face recognition from still frontal images. In this paper, we introduce the CohortLDA baseline algorithm, which is an Linear Discriminant Analysis (LDA) algorithm with color spaces and cohort normalization. CohortLDA greatly outperforms some well known face recognition algorithms on the GBU challenge problem. The GBU protocol includes rules for creating training sets. We investigate the effect on performance of violating the rules for creating training sets. This analysis shows that violating the GBU protocol can substantially over estimate performance on the GBU challenge problem.

关键词： Studies Good Bad Facial recognition face recognition algorithm color spaces

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BOOSTING LOCAL DESCRIPTORS FOR MATCHING COMPOSITE AND DIGITAL face IMAGES

BOOSTING LOCAL DESCRIPTORS FOR MATCHING COMPOSITE AND DIGITA...

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IEEE International Conference on Image Processing

作者： Paritosh Mittal Aishwarya Jain Richa Singh Mayank Vatsa IIIT-Delhi India

ISBN: (纸本)9781479923427

Sketch recognition is one of the most challenging applications of face recognition. Due to the incorrectness of features in the witness description, standard face recognition algorithms are generally not applicable to matching sketches with digital face images. This research designs a patch based face recognition algorithm that generates patches around fiducial features and extracts local information from these patches using Daisy descriptor. The information extracted from these patches are then efficiently matched using GentleBoostKO algorithm. The experiments performed on the PRIP composite face image database show that the proposed algorithm yields promising results and outperforms existing state-of-the-art algorithms and a commercial system.

关键词： Sketch recognition composite faces daisy descriptor face recognition algorithm face Patches images Composites Dosage Form Not Applicable Descriptors local information. Sketches Facial recognition NCOA6 protein, human

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Robust face recognition Method Based on SIFT Features Using Levenberg-Marquardt Backpropagation Neural Networks

Robust Face Recognition Method Based on SIFT Features Using ...

引用

International Congress on Image and Signal Processing

作者： Gautam Narang Soumya Singh Arjun Narang Department of Electrical and Electronics Engineering Bharati Vidyapeeth's College of Engineering Department of Electronics and Instrumentation Birla Institute of Technology and Science

ISBN: (纸本)9781479927654

face recognition from image or video is a popular topic in biometrics research. It has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying facial images and assigning them to persons in a database. This paper proposes an efficient face recognition method based on Scale Invariant Feature Transform (SIFT) for feature extraction and using Levenberg-Marquardt Backpropagation (LMBP) neural network for classification. In this proposed method, we assign the extracted SIFT features of the face images as input vectors to our neural network instead of using just the raw data as the input. Experiments performed on the Yale face database show that the facial images can be recognized by the proposed face identification method efficiently. Also, the traditional face recognition algorithms are compared with the proposed algorithm to show its effectiveness.

关键词： face recognition SIFT features Levenberg-Marquardt Neural network face recognition techniques Facial recognition Neural network Mug shots back propagation face recognition algorithm Biometrics

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GRAPH REGULARIZED DISCRIMINANT ANALYSIS AND ITS APPLICATION TO face recognition

GRAPH REGULARIZED DISCRIMINANT ANALYSIS AND ITS APPLICATION ...

引用

IEEE International Conference on Image Processing

作者： Tianfei Zhou Yao Lu Yanan Zhang Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology

ISBN: (纸本)9781479983407

Linear Discriminant Analysis (LDA) is a powerful technology for supervised dimensionality reduction, however, it only captures the extrinsic (or global) structure in the data and fails to discover the intrinsic structure of the data manifold. In this paper, we develop a new linear supervised dimensionality reduction method, called Graph Regularized Discriminant Analysis (GRDA), which respects both extrinsic and intrinsic structure in the data. In particular, a regularization term, incorporating the manifold structure, is introduced into the objective function of LDA. The formulation allows us to achieve a more discriminative subspace by simultaneously considering the graph preserving and the global LDA criteria. We then apply the proposed GRDA algorithm to face recognition by exploiting the local dissimilarity of face images in different classes. Experimental results clearly show that the proposed GRDA method outperforms many state-of-the-art face recognition algorithms.

关键词： Linear Discriminant Analysis Dimensionality reduction Regularization face recognition dimensionality reduction Linear discriminant analysis Facial recognition face recognition algorithm laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum face global structures

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face recognition with Enhanced Local Gabor Binary Pattern from Human Fixations

Face Recognition with Enhanced Local Gabor Binary Pattern fr...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Eunsoo Choi Seong-Whan Lee Christian Wallraven Department of Computer Science and Engineering Korea University Department of Brain and Cognitive Engineering Korea University

ISBN: (纸本)9781467317139

Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.

关键词： face recognition Human perception LGBP Eye-tracking Lighting changes Facial recognition face recognition algorithm binary pattern face face recognition (Psychology) Computers Lighting

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AUTOMATIC face recognition FROM VIDEO SEQUENCES USING A TEMPLATE BASED CROSS CORRELATION METHOD

AUTOMATIC FACE RECOGNITION FROM VIDEO SEQUENCES USING A TEMP...

引用

IEEE Canadian Conference on Electrical and Computer Engineering

作者： Edward Rosales Yun Tie Anastasios Venetsanopoulos Ling Guan Electrical Engineering Ryerson University Toronto ON Canada

ISBN: (纸本)9781479900312

face recognition in videos has been an active topic in the field of object recognition and computer vision. In this paper we propose an automatic face recognition algorithm from video sequences using a template based cross correlation (TBCC) method. It utilizes random selection of frames to form the training template for the discriminant feature representation of a face. The proposed method was tested by randomly selecting a subject from the RML and Cohn-Kanade (CK) databases with non-overlapping sequences between test and training sequences. From the experiments on several datasets, the system performance achieved an average recognition rate of 88%.

关键词： Facial recognition Video sequences jigs(templates) Videotapes Cross correlation training sequence Randomisation object recognition images computer vision AUTOMATIC System performance face recognition algorithm

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ON RANK AGGREGATION FOR face recognition FROM VIDEOS

ON RANK AGGREGATION FOR FACE RECOGNITION FROM VIDEOS

引用

IEEE International Conference on Image Processing

作者： Himanshu S. Bhatt Richa Singh Mayank Vatsa IIIT-Delhi India

ISBN: (纸本)9781479923427

face recognition from still face images suffers due to intra-personal variations caused by pose, illumination, and expression that degrade the performance. On the other hand, videos provide abundant information that can be leveraged to compensate the limitations of still face images and enhance face recognition performance. This paper presents a video based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images. The video signature embeds diverse intra-personal and temporal variations across multiple frames, thus facilitates matching two videos with large variations. Two videos are matched by comparing their discriminative signatures using the Kendall tau similarity distance measure. Performance comparison with the benchmark results and a commercial face recognition system on the publicly available YouTube faces database show the efficacy of the proposed video based face recognition algorithm.

关键词： Video based face recognition Rank aggregation Dictionary based face recognition Videotapes Facial recognition face recognition algorithm face ordered list face recognition system images face

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Analysis of face recognition technology based on deep learning

Analysis of face recognition technology based on deep learni...

引用

作者： Xinya Wang School of Software Dalian University of Foreign Languages

The emergence of face recognition technology has brought great convenience to human society. After years of research and improvement, face recognition technology has matured. This paper mainly studies face recognition technology based on deep learning, and introduces five main applications, including occlusion face recognition, 3D face recognition,and expression recognition. The typical algorithms listed show that, compared with the traditional feature face algorithms and local binary patterns, algorithms such as Deepface,faceNet, ResNet, etc. have made significant progress in recognition efficiency and accuracy in recent years. After combining the development status of face recognition technology with the problems and security risks of the current technology, this paper proposes three solutions:increasing the amount of data, optimizing the model, and government control. In the future,legal data sharing and code open source will be major progress in this field, and dynamic face recognition technology will also be widely used.

关键词： deep learning face recognition algorithm problems and solutions prospects

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Energy-efficient face recognition Authentication System Using Human Detection IoT Modules

Energy-efficient Face Recognition Authentication System Usin...

引用

International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Nikhil Rao Mohammad Hasnain Rajan Keith Rebello Swapnil Gharat MCT's Rajiv Gandhi Institute of Technology Mumbai India

ISBN: (数字)9781728123394

ISBN: (纸本)9781728123400

The application of effective techniques like facial recognition and object detection in the domain of IoT has revolutionized the levels of control and accuracy sensors have had for a variety of purposes. face recognition modules have established their immense usefulness, and their impact has overweighed most other traditional forms of access control. It remains one of the most secure methods to authenticate human beings to grant them access to restricted areas or systems in an organization. However, this incomparable rise in accuracy comes with a relatively huge price. The algorithms used in facial recognition are complex mathematical functions that are iteratively performed on vast volumes of data continuously. In IoT applications, the embedded systems that run these algorithms are at all times clocked to their maximum processing power, they need not run in cases when there are no humans are present in the vicinity. This is inefficient, especially in systems that need to perform face recognition round-the-clock for authentication. This paper proposes a simple, intuitive and efficient solution to conserve the processor from being clocked to its maximum throughput without compromising on the high level of security that the face recognition algorithm offers.

关键词： Home Automation Image Processing Deep Learning Energy-Efficiency face recognition Facial recognition Energy efficiency home automation Image processing Security level face recognition algorithm Human Object detection

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