scholarly journals Long distance face recognition for enhanced performance of internet of things service interface

2014 ◽  
Vol 11 (3) ◽  
pp. 961-974 ◽  
Author(s):  
Hae-Min Moon ◽  
Sung Pan
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Human Life ◽  
Recognition Algorithm ◽  
Security Systems ◽  
Long Distance ◽  
Training Images ◽  
Face Images ◽  
Euclidean Distance Measure ◽  
The Face

As many objects in the human ambient environment are intellectualized and networked, research on IoT technology have increased to improve the quality of human life. This paper suggests an LDA-based long distance face recognition algorithm to enhance the intelligent IoT interface. While the existing face recognition algorithm uses single distance image as training images, the proposed algorithm uses face images at distance extracted from 1m to 5m as training images. In the proposed LDA-based long distance face recognition algorithm, the bilinear interpolation is used to normalize the size of the face image and a Euclidean Distance measure is used for the similarity measure. As a result, the proposed face recognition algorithm is improved in its performance by 6.1% at short distance and 31.0% at long distance, so it is expected to be applicable for USN?s robot and surveillance security systems.

scholarly journals Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhixue Liang
Keyword(s):  
Face Recognition ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Delivery Device ◽  
Face Images ◽  
Region Extraction ◽  
The Face ◽  
Enhancement Algorithm

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

scholarly journals Three-dimensional face recognition using variance-based registration and subject-specific descriptors

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985171 ◽  
Author(s):  
Naeem Iqbal Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Nouman Ali ◽  
Anzar Mahmood ◽  
...  
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Superior Performance ◽  
Data Sets ◽  
Face Images ◽  
Subject Specific ◽  
The Face ◽  
The Subject ◽  
Depth Curves

Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.

Face Recognition Algorithm Based on 3D Point Cloud Acquired by Mixed Image Sensor

2019 ◽  
Vol 23 (2) ◽  
pp. 366-369
Author(s):  
Tang-Tang Yi ◽  
Keyword(s):  
Face Recognition ◽  
Point Cloud ◽  
Recognition Accuracy ◽  
Image Sensor ◽  
Recognition Algorithm ◽  
Face Image ◽  
3D Point Cloud ◽  
3D Face ◽  
Face Images ◽  
The Face

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.

scholarly journals RECOGNIZING FACES USING INDIVIDUAL EIGEN SUBSPACES TECHNIQUE WITH FUZZY RULES METHODOLOGY

2015 ◽  
pp. 164-169
Author(s):  
AISHWARYA P ◽  
KARNAN MARCUS
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Rejection Rate ◽  
Security Systems ◽  
Major Objective ◽  
Face Representation ◽  
Face Images ◽  
The Face ◽  
Small Set ◽  
Effective Analysis

This paper proposes a new methodology of recognizing face using Individual Eigen Subspaces and it’s implemented in the field of Image Processing for Personnel verification or recognition. A major objective of this work is to develop a tool for face recognition, which can help in quicker and effective analysis of a face from the face set, thus reducing false acceptance rate and false rejection rate. Face recognition has been widely explored in the past years. A lot of techniques have been applied in various applications. Robustness and reliability have become more and more important for these applications especially in security systems. In this thesis, a variety of approaches for face recognition are reviewed first. These approaches are classified according to three basic tasks: face representation, face detection, and face identification. An implementation of the appearance-based face recognition method, the eigenface recognition approach, is reported. This method utilizes the idea of the principal component analysis and decomposes face images into a small set of characteristic feature images called eigenfaces. This proposed work is intended to develop, multiple face Eigen subspaces. With each one is corresponding to one known subject privately, rather than all individuals sharing one universal subspace as in the traditional eigenface method. Compared with the traditional single subspace face representation, the proposed method captures the extra personal difference to the most possible extent, which is crucial to distinguish between individuals, and on the other hand, it throws away the most intrapersonal difference and noise in the input.

scholarly journals Encrypted face recognition algorithm based on Ridgelet-DCT transform and THM chaos

2021 ◽  
Vol 19 (2) ◽  
pp. 1373-1387
Author(s):  
Zilong Liu ◽  
◽  
Jingbing Li ◽  
Jing Liu ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Homomorphic Encryption ◽  
Image Data ◽  
Recognition Algorithm ◽  
Personal Privacy ◽  
Dct Transform ◽  
Face Images ◽  
Potential Safety ◽  
The Face ◽  
Encryption Method

<abstract> <p>With the popularization and application of face recognition technology, a large number of face image data are spread and used on the Internet. It has brought great potential safety hazard for personal privacy. Combined with the characteristics of tent chaos and Henon chaos, a THM (tent-Henon map) chaotic encrypted face algorithm based on Ridgelet-DCT transform is proposed in this paper. Different from conventional face recognition methods, this new approach encryptes the face images by means of using the homomorphic encryption method to extract their visual robust features in the first place, and then uses the proposed neural network model to design the encrypted face recognition algorithm. This paper selects the ORL face database of Cambridge University to verify the algorithm. Experimental results show that the algorithm has a good performance in encryption effect, security and robustness, and has a broad application prospect.</p> </abstract>

SUBSPACE-BASED FACE RECOGNITION: OUTLIER DETECTION AND A NEW DISTANCE CRITERION

2005 ◽  
Vol 19 (04) ◽  
pp. 479-493 ◽  
Author(s):  
PEI CHEN ◽  
DAVID SUTER
Keyword(s):  
Face Recognition ◽  
Outlier Detection ◽  
Principal Component ◽  
Dimensional Subspace ◽  
Recognition Algorithm ◽  
New Techniques ◽  
Face Images ◽  
The Face ◽  
Low Dimensional ◽  
New Strategies

Illumination effects, including shadows and varying lighting, make the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions approximately lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies.

scholarly journals A Novel Face Recognition Algorithm Using Gabor - based KPCA

2018 ◽  
Vol 7 (2) ◽  
pp. 124
Author(s):  
Umasankar Ch ◽  
D. Naresh Kumar ◽  
Md. Abdul Rawoof ◽  
D. Khalandar Basha ◽  
N. Madhu
Keyword(s):  
Face Recognition ◽  
Structural Characteristics ◽  
Feature Space ◽  
Facial Feature ◽  
Input Image ◽  
Recognition Algorithm ◽  
Nonlinear Mapping ◽  
Kernel Pca ◽  
Face Images ◽  
The Face

The Gabor wavelets are used to extract facial features, and then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this work, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical properties of the input features, but also adopts an Eigen mask to emphasize those important facial feature points The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database.

scholarly journals Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Joseph A. Mensah
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Environmental Constraints ◽  
Biometric Data ◽  
Face Images ◽  
The Face ◽  
Left And Right ◽  
Value Decomposition ◽  
User Friendly

Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.

scholarly journals Masked Face Recognition Algorithm for a Contactless Distribution Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
GuiLing Wu
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Attention Mechanism ◽  
Human Contact ◽  
Face Images ◽  
The Face ◽  
Common Application ◽  
Image Part

A contactless delivery cabinet is an important courier self-pickup device, for the reason that COVID-19 can be transmitted by human contact. During the pandemic period of COVID-19, wearing a mask to take delivery is a common application scenario, which makes the study of masked face recognition algorithm greatly significant. A masked face recognition algorithm based on attention mechanism is proposed in this paper in order to improve the recognition rate of masked face images. First, the masked face image is separated by the local constrained dictionary learning method, and the face image part is separated. Then, the dilated convolution is used to reduce the resolution reduction in the subsampling process. Finally, according to the important feature information of the face image, the attention mechanism neural network is used to reduce the information loss in the subsampling process and improve the face recognition rate. In the experimental part, the RMFRD and SMFRD databases of Wuhan University were selected to compare the recognition rate. The experimental results show that the proposed algorithm has a better recognition rate.

SPGAN: Face Forgery Using Spoofing Generative Adversarial Networks

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-20
Author(s):  
Yidong Li ◽  
Wenhua Liu ◽  
Yi Jin ◽  
Yuanzhouhan Cao
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Generative Adversarial Networks ◽  
Long Distance ◽  
Face Region ◽  
Face Images ◽  
Face Recognition System ◽  
The Face ◽  
Textural Attributes ◽  
Cost Efficient

Current face spoof detection schemes mainly rely on physiological cues such as eye blinking, mouth movements, and micro-expression changes, or textural attributes of the face images [9]. But none of these methods represent a viable mechanism for makeup-induced spoofing, especially since makeup has been widely used. Compared with face alteration techniques such as plastic surgery, makeup is non-permanent and cost efficient, which makes makeup-induced spoofing become a realistic threat to the integrity of a face recognition system. To solve this problem, we propose a generative model to construct spoofing face images (confusing face images) for improving the accuracy and robustness of automatic face recognition. Our network structure is composed of two separate parts, with one using inter-attention mechanism to obtain interested face region, and another using intra-attention to translate imitation style with preserving imitation style-excluding details. These two attention mechanisms can precisely learn imitation style, where inter-attention pays more attention to imitation regions of image and intra-attention learns face attributes with long distance in image. To effectively discriminate generated images, we introduce an imitation style discriminator. Our model (SPGAN) generates face images that transfer the imitation style from target to subject image and preserve the imitation-excluding features. Experimental results demonstrate the performance of our model in improving quality of imitated face images.

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