Face Recognition Based on Deep Learning

Wang, Weihong; Yang, Jie; Xiao, Jianwei; Li, Sheng; Zhou, Dixin

doi:10.1007/978-3-319-15554-8_73

Cited by 75 publications

(30 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sajid et al [26] extracted facial-asymmetry-based demographic informative features to evaluate the age group, gender, and race of a given face image by employing two well-known face datasets, MORPH II and FERET. Wang et al [27] proposed the deep learning technique to obtain facial landmark detection and limitless face recognition. To overcome the face landmark detection issue, they offered a layer-by-layer training technique of a deep convolutional neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

View full text Add to dashboard Cite

Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

View full text Add to dashboard Cite

show abstract

“…Deep Learning [4] algorithms have been successfully applied to image recognition problems. Deep learning involves neural networks with more than one hidden layer, has been used successfully in face recognition, speech recognition and natural language processing problems [5,6]. Deep learning successfully implemented for human gesture recognition in recent years.…”

Section: Introductionmentioning

confidence: 99%

Sign Language Semantic Translation System using Ontology and Deep Learning

Elsayed¹,

Fathy²

2020

IJACSA

View full text Add to dashboard Cite

Translation and understanding sign language may be difficult for some. Therefore, this paper proposes a solution to this problem by providing an Arabic sign language translation system using ontology and deep learning techniques. That is to interpret user's signs to different meanings. This paper implemented ontology on the sign language domain to solve some sign language challenges. In this first version, simple static signs composed of Arabic alphabets and some Arabic words started to translate. Deep Convolution Neural Network (CNN) architecture was trained and tested on a pre-made Arabic sign language dataset and on a dataset collected in this paper to obtain better accuracy in recognition. Experimental results show that according to the pre-made Arabic sign language dataset the classification accuracy of the training set (80% of the dataset) was 98.06% and recognition accuracy of the testing set (20% of the dataset) was 88.87%. According to the collected dataset, the classification accuracy of the training set was 98.6% and Semantic recognition accuracy of the testing set was 94.31%.

show abstract

“…Currently, face recognition has become a hot topic in the research fields such as pattern recognition, image processing, machine vision, neural networks and cognitive science [1,2]. FERET test showed that the changing of illumination and pose is the major challenge of current face recognition system [3].…”

Section: Introductionmentioning

confidence: 99%

Face Illumination Processing Using Wavelet Transform and Gradientfaces

Xue¹,

Yang²,

Nie³

2017

dtcse

View full text Add to dashboard Cite

Abstract. In view of the problem of illumination, a new approach based on wavelet transform and gradientfaces for illumination processing is presented. Firstly, the method calculates wavelet transform and multilevel wavelet decomposition in logarithm domain. The low frequency coefficient is discarded partially. After reconstructed, the high frequency component of the face image is enhanced using gradientfaces. Secondly, the face invariant feature is extracted by Principal Component Analysis (PCA), the nearest neighborhood classifier using cosine distance is adopted for face classification. The experiment result on Yale B frontal face database demonstrates that the presented algorithm could recognize all of the test samples and the face recognition rate is 100%. Thus, this technique can overcome the effect of different degree of illumination efficiently.

show abstract

Face Recognition Based on Deep Learning

Cited by 75 publications

References 6 publications

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

Sign Language Semantic Translation System using Ontology and Deep Learning

Face Illumination Processing Using Wavelet Transform and Gradientfaces

Contact Info

Product

Resources

About