Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

An Empirical Evaluation of Name Semantic Network for Face Annotation

Author(s): Kasthuri Anburajan*, Suruliandi Andavar and Poongothai Elango

Volume 13, Issue 4, 2020

Page: [557 - 571] Pages: 15

DOI: 10.2174/2213275912666190204141902

Price: $65

Abstract

Background: Face annotation is the naming procedure to assign the correct name of a person who has emerged on an image.

Objective: The main objective of this paper was to compare and evaluate six feature extraction techniques for face annotation under real-time challenging images and to find the best suitable feature for face annotation.

Method: From literature review, it has been observed that Name Semantic Network (NSN) outperforms other annotation methods for various unconditioned images as well as ambiguous tags. However, the NSN’s performance can differ with various feature extraction techniques. Hence, its success is influenced by the feature extraction techniques that are used. Therefore, in this work, the NSN’s performance is experimented and evaluated with various feature extraction methods such as the Discrete Cosine Transform Local Binary Pattern (DCT-LBP), Discrete Fourier Transform Local Binary Pattern (DFT-LBP), Local Patterns of Gradients (LPOG), Gist, Local Order-constrained Gradient Orientations (LOGO) and Convolutional Neural Networks (CNNs) deep features.

Results: Different feature extraction approaches demonstrate variations in performance with respect to a range of difficulties in face annotation using the Yahoo, LFW and IMFDB databases. The experimental results show that the deep feature method can achieve better recognition rate other than texture features. It confronts several issues in the presentation of a face in an image and produces better results.

Conclusion: It is concluded that the CNNs deep feature is the best feature extraction technique that offers enhanced performance for face annotation.

Keywords: Name semantic network, convolutional neural networks, features analysis, deep features, texture features, face annotation.

Graphical Abstract

[1]
Z. Lu , Z. Fu , T. Xiang , P. Han , L. Wang , and X. Gao . ,"Learning from weak and noisy labels for semantic segmentation". IEEE Transac Pattern Analy Machine Intell Vol. 39, No. 3, pp. 486-500, 2017.
[2]
G. Papandreou , L.C. Chen , K.P. Murphy , and A.L. Yuille . ,"Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation", In: Proceedings of the IEEE International Conference on Computer Vision 2015; 1742-50.
[3]
F. Tian , X. Shen . ,"Image annotation with weak labels". In: International Conference on Web-Age Information Management Berlin, Heidelberg: Springer 2013 pp; 375-80.
[4]
D. Wang , S.C. Hoi , Y. He , J. Zhu , T. Mei , and J. Luo . ,"Retrieval-based face annotation by weak label regularized local coordinate coding", IEEE Transac Pattern Analy Machine Intell Vol. 36, No. 3, pp. 550-563 2014
[5]
J. Han , J. Hu , and W. Deng . ,"Constrained spectral clustering on face annotation system", In: Chinese Conference on Pattern Recognition Springer, Singapore, Nov 2016; 3-12.
[6]
P.T. Pham , M. Marie-Francine , and T. Tinne . "Cross-media alignment of names and faces", IEEE Transac Multimedia Vol. 12, No.1, pp. 13-27, 2010.
[7]
X. Su , J. Peng , X. Feng , and J. Wu . ,"Labeling faces with names based on the name semantic network", Multimedia Tools Appl Vol. 75, No. 11, pp. 6445-6462, 2016.
[8]
S. Xiao , D. Xu , and J. Wu . ,"Automatic face naming by learning discriminative affinity matrices from weakly labeled images". IEEE Transact Neural Netw Learn Syst Vol. 26, No. 10, pp. 2440-2452, 2015.
[9]
D. Wang , S.C. Hoi , P. Wu , J. Zhu , Y. He , and C. Miao . ,"Learning to name faces: A multimodal learning scheme for search-based face annotation", In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval 2013; 443-52.
[10]
J. Zhu , S.C. Hoi , and M.R. Lyu . ,"Face annotation using transductive kernel fisher discriminant”, IEEE Transac Multimedia Vol. 10, No. 1, pp. 86-96, 2007.
[11]
H. Zitouni , M.F. Bulut . , and P Duygulu, “Recognizing faces in news photographs on the web” 24th International Symposium on Computer and Information Sciences ISCIS 2009 pp; 50-5.
[12]
B. Zhang , Y. Gao , S. Zhao , & J. Liu . ,"Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor”, IEEE Transac Image Processing Vol. 19, No. 2, pp. 533-544, 2010
[13]
S.H. Lee , J.Y. Choi , Y.M. Ro , and K.N. Plataniotis . ,"Local color vector binary patterns from multichannel face images for face recognition", IEEE Transact Image Processing Vol. 21, No. 4, pp. 2347-53, 2012.
[14]
F. Juefei-Xu , and M. Savvides . ,"Subspace-based discrete transform encoded local binary patterns representations for robust periocular matching on NIST’s face recognition grand challenge", IEEE Transac Image Processing Vol. 23, No. 8, pp. 3490-3505, 2014.
[15]
H.T. Nguyen , and A. Caplier . ,"Local patterns of gradients for face recognition", IEEE Trans Info Forens Security Vol. 10, No. 8, pp. 1739-1751, 2015.
[16]
A. Oliva , and A. Torralba . ,"Building the gist of a scene: The role of global image features in recognition", Prog Brain Res Vol. 155, pp. 23-36, 2006
[17]
C. Ren . X, Z. Lei, D. Q. Dai, and S. Z. Li, “Enhanced local gradient order features and discriminant analysis for face recognition", IEEE Transac Cybernet Vol. 46, No. 11, pp. 2656-2669, 2016.
[18]
M. Guo , M. Xiao , and D. Gong . ,"Face recognition using deep convolutional neural network in cross-database study", In: International Conference on Intelligent and Interactive Systems and Applications Springer, Cham, Jun 2017 pp; 387-93.
[19]
H. Khalajzadeh , M. Mansouri , and M. Teshnehlab . ,"Face recognition using convolutional neural network and simple logistic classifier",In: Soft Computing in Industrial Applications Springer, Cham,. 2014; pp. 197-207.
[20]
Z. Lu , X. Jiang , and A. Kot . ,"Enhance deep learning performance in face recognition. ” In Image, Vision and Computing (ICIVC), IEEE 2nd International Conference, 2017 PP; 244-8.
[21]
W. Wang , J. Yang , J. Xiao , S. Li , and D. Zhou . ,"Face recognition based on deep learning", In: International Conference on Human Centered Computing Springer, Cham 2014 pp; 812-20.
[22]
J. Susskind , V. Mnih , and G. Hinton . ,"On deep generative models with applications to recognition", In Computer Vision and Pattern Recognition (CVPR), IEEE Conference 2011; 2857-64.
[23]
Z. Zhu , P. Luo , X. Wang , and X. Tang . ,"Deep learning identity-preserving face space", In: Proceedings of the IEEE International Conference on Computer Vision 2013; 113-20.
[24]
Y. Sun , X. Wang , and X. Tang . ,"Hybrid deep learning for face verification", Proceedings of the IEEE International Conference on Computer Vision 1489-96.2013;
[25]
B. Liu , X. Yu , P. Zhang , A. Yu , Q. Fu , and X. Wei . ,"Supervised deep feature extraction for hyperspectral image classification", IEEE Trans Geosci Remote Sens Vol. 56, No. 4, pp. 1909-1921, 2018.
[26]
V. Ojansivu , and J. Heikkila . ,"Blur insensitive texture classification using local phase quantization", In: International Conference on Image and Signal Processing Springer, Berlin, Heidelberg, 2008 pp; 236-43.
[27]
P. Moreno , A. Bernardino , and J. Santos-Victor . ,"Gabor parameter selection for local feature detection", In: Iberian Conference on Pattern Recognition and Image Analysis Springer, Berlin, Heidelberg, 2005 pp; 11-9.
[28]
G. Tzimiropoulos , S. Zafeiriou , and M. Pantic . ,"Subspace learning from image gradient orientations", IEEE Transac Pattern Analy Machine Intell Vol. 34, No. 12, pp. 2454-2466, 2012.
[29]
Z. Wang , B. Fan , and F. Wu . ,"Local intensity order pattern for feature description In Computer Vision (ICCV), IEEE International Conference IEEE", 2011 pp; 603-10.

Rights & Permissions Print Cite
Article Metrics
6
© 2024 Bentham Science Publishers | Privacy Policy