A robust face recognition algorithm under varying illumination using adaptive retina modeling

2013 ◽  
Author(s):  
Yuen Kiat Cheong ◽  
Vooi Voon Yap ◽  
Humaira Nisar
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
Robust Face Recognition

scholarly journals A New Evolutionary-Incremental Framework for Feature Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Mohamad-Hoseyn Sigari ◽  
Muhammad-Reza Pourshahabi ◽  
Hamid-Reza Pourreza
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Algorithm Design ◽  
Feature Selection Method ◽  
Recognition Algorithm ◽  
Comparison Result ◽  
Open Problems ◽  
Invasive Weed ◽  
Np Hard Problem ◽  
Robust Face Recognition

Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

scholarly journals Robust face recognition based on multi-task convolutional neural network

2021 ◽  
Vol 18 (5) ◽  
pp. 6638-6651
Author(s):  
Huilin Ge ◽  
◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Recognition Algorithm ◽  
Face Images ◽  
Robust Face Recognition

<abstract> <sec><title>Purpose</title><p>Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN).</p> </sec> <sec><title>Methods</title><p>In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN.</p> </sec> <sec><title>Results</title><p>The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect.</p> </sec> <sec><title>Conclusions</title><p>This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.</p> </sec> </abstract>

OCCLUSION ROBUST FACE RECOGNITION WITH SCATTERING OPERATOR IN GRADIENT DOMAIN

2013 ◽  
Vol 11 (01) ◽  
pp. 1350001 ◽  
Author(s):  
BIN XU ◽  
ZHAO WEI SHANG ◽  
YUAN YAN TANG ◽  
BIN FANG ◽  
TAI PING ZHANG
Keyword(s):  
Face Recognition ◽  
Structural Information ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Scattering Operator ◽  
Translation Invariant ◽  
Gradient Domain ◽  
Deformation Stability ◽  
Scattering Operators ◽  
Robust Face Recognition

In this paper, we propose a novel occlusion robust face recognition algorithm in gradient direction domain (GDD) using scattering operator. The proposed algorithm transforms image into the GDD to remove pseudo-edges, then scattering operator is used to extract face feature from face image in GDD. Since scattering operators can effectively extract the structural information in face owing to locally translation invariant and deformation stability, the proposed approach is robust to occlusion and varying expression. Our scheme has demonstrated the state-of-the-art performance on several datasets. Especially, our method on the sunglasses images and the scarf in AR database achieves a recognition rate of 100 and 95% respectively, which significantly outperforms most existing methods.

Hybrid Face Recognition Algorithm for Wireless Sensor Network

2017 ◽  
Vol 13 (3) ◽  
pp. 267-281
Author(s):  
Matheel E. Abdulmunem E. Abdulmunem ◽  
◽  
Fatima B. Ibrahim
Keyword(s):  
Face Recognition ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Recognition Algorithm ◽  
Wireless Sensor
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