scholarly journals Design and development of a parallelized algorithm for face recognition in mobile cloud environment

2021 ◽  
Vol 10 (1) ◽  
pp. 47
Author(s):  
K. N. Bhatt ◽  
Sanket S Naik Dessai ◽  
V. S. Yerragudi
Keyword(s):  
Face Recognition ◽  
Data Storage ◽  
Recognition Algorithm ◽  
Training Dataset ◽  
Mobile Cloud ◽  
Task Parallelism ◽  
Cloud Environment ◽  
Huge Data ◽  
Cloud Server ◽  
The Face

<p>Face recognition is the biometric application to recognise the identity. Face recognition application holds a set of images which are called databases stored by the user at cloud database. Cloud computing environment, database can be stored in the cloud environment to achieve huge data storage area. The problem with these data storages are that because of that huge size processing on this storage takes too much of compiling time. This paper aims to develop face recognition in mobile cloud environment by exploiting data or task parallelism in existing face recognition algorithms. To design and develop parallel PCA based face recognition algorithm. The parallel PCA face recognition algorithm has been deployed in the cloud server for performing PCA by request of user. It matches the image on the cloud server and gives response back to the user in the fewer amounts of time and with reduced latency. The developed Parallel PCA face recognition algorithm has minimized the overall response time for the face recognition algorithm. The performance of the developed system is tested and analysed on real face images. To analyse the developed system, a centralized and distributed based server methods are developed and comparison is being carried out. The conclusion drawn that the distributed server improves the efficiency as well as the computing power as compared with centralized server system. The comparison of centralized and distributed based servers is carried out by observing the time taken while varying the number of images in the training dataset.</p>

scholarly journals Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhe-Zhou Yu ◽  
Yu-Hao Liu ◽  
Bin Li ◽  
Shu-Chao Pang ◽  
Cheng-Cheng Jia
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Information Needs ◽  
Nonnegative Matrix Factorization ◽  
Recognition Rate ◽  
Nonnegative Matrix ◽  
Recognition Algorithm ◽  
Incremental Method ◽  
Real World Application ◽  
The Face

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

2015 ◽  
Vol 5 (1) ◽  
pp. 66-81 ◽  
Author(s):  
M. Parisa Beham ◽  
S. M. Mansoor Roomi ◽  
J. Alageshan ◽  
V. Kapileshwaran
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
K Nearest Neighbor ◽  
Research Issues ◽  
Histograms Of Oriented Gradients ◽  
Recognition Phase ◽  
The Face ◽  
Pose Variation

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

Application of Gabor Dictionary and SL0 Norm in Disguise Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1211-1214
Author(s):  
Jun Ying Zeng ◽  
Jun Ying Gan ◽  
Yi Kui Zhai
Keyword(s):  
Face Recognition ◽  
Function Approximation ◽  
Multiple Scales ◽  
Recognition Rate ◽  
Principal Component ◽  
Facial Feature ◽  
Training Image ◽  
Recognition Algorithm ◽  
The Face ◽  
Gabor Dictionary

A fast sparse representation face recognition algorithm based on Gabor dictionary and SL0 norm is proposed in this paper. The Gabor filters, which could effectively extract local directional features of the image at multiple scales, are less sensitive to variations of illumination, expression and camouflage. SL0 algorithm, with the advantages of calculation speed，require fewer measurement values by continuously differentiable function approximation L0 norm and reconstructed sparse signal by minimizing the approximate L0 norm. The algorithm obtain the local feature face by extracting the Gabor face feature, reduce the dimensions by principal component analysis, fast sparse classify by the SL0 norm. Under camouflage condition, The algorithm block the Gabor facial feature and improve the speed of formation of the Gabor dictionary. The experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate to some extent and can generalize well to the face recognition, even with a few training image per class.

BSS: block-based sharing scheme for secure data storage services in mobile cloud environment

2014 ◽  
Vol 70 (2) ◽  
pp. 946-976 ◽  
Author(s):  
Abdul Nasir Khan ◽  
M. L. Mat Kiah ◽  
Mazhar Ali ◽  
Sajjad A. Madani ◽  
Atta ur Rehman Khan ◽  
...  
Keyword(s):  
Data Storage ◽  
Mobile Cloud ◽  
Cloud Environment ◽  
Sharing Scheme ◽  
Secure Data ◽  
Block Based

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.

Study on face recognition under unconstrained conditions based on LBP and deep learning

2020 ◽  
pp. 1-12
Author(s):  
Liping Zhou ◽  
Mingwei Gao ◽  
Chun He
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Input Data ◽  
Recognition Rate ◽  
Texture Feature ◽  
Recognition Algorithm ◽  
Correct Recognition ◽  
Layer By Layer ◽  
The Face ◽  
Correct Recognition Rate

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition.

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.

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