nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

10. Beauty Analysis by Learning Machine and Subspace Extension

verfasst von : David Zhang, Fangmei Chen, Yong Xu

Erschienen in: Computer Models for Facial Beauty Analysis

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Compared with features based methods, dedicated data-driven facial beauty modeling methods have a major advantage that it may be able to directly perform facial beauty analysis from raw data.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A New Hypothesis on Facial Beauty Perception

Nächstes Kapitel Combining a Causal Effect Criterion for Evaluation of Facial Beauty Models

Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525–536MathSciNetCrossRefMATH

Belhumeur PN, Hespanha J, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRef

Bohné J, Ying Y, Gentric S, Pontil M (2014) Large margin local metric learning. In: Proceedings of European conference on computer vision, pp 679–694

Cao Q, Ying Y, Li P (2013) Similarity metric learning for face recognition. In: Proceedings of IEEE international conference on computer vision, pp 2408–2415

Chacko BP, Krishnan VV, Raju G, Anto PB (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybernet 3(2):149–161CrossRef

Civicioglu P (2013) Backtracking search optimization llgorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144MathSciNetCrossRefMATH

Davis J, Kulis B, Jain P, Sra S, Dhillon I (2007) Information theoretic metric learning. In: Proceedings of international conference on Machine learning, pp 209–216

Deng WY, Zheng QH, Wang ZM (2014) Cross-person activity recognition using reduced kernel extreme learning machine. Neural Networks 53:1–7CrossRef

Eisenthal Y, Dror G (2006) Facial attractiveness: beauty and the machine. Neural Comput 18(1):119–142CrossRef

Fan J, Chau KP, Wan X, Zhai L, Lau E (2012) Prediction of facial attractiveness from facial proportions. Pattern Recogn 45(6):2326–2334CrossRef

Fu Y, Huang TS (2008) Human age estimation with regression on discriminative aging manifold. IEEE Trans Multimedia 10(4):578–584CrossRef

Geng X, Zhou ZH, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234–2240CrossRef

Gray D, Yu K, Xu W (2010) Predicting facial beauty without landmarks. In: Proceedings of European conference on computer vision, pp 434–447

Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. In: Proceedings of IEEE international conference on computer vision, pp 498–505

Guo G, Fu Y, Dyer C, Huang T (2008) Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process 17(7):1178–1188MathSciNetCrossRef

He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRef

Huang G, Ramesh M, Berg T, Learned-Miller E (2007) Labeled Faces in the Wild: a database for studying face recognition in unconstrained environments. Technical Report, Amherst

Huang GB, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16):3056–3062CrossRef

Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16):3460–3468CrossRef

Huang GB, Chen L, Siew CK (2006a) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Networks 17(4):879–892CrossRef

Huang GB, Zhu QY, Siew CK (2006b) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRef

Huang GB, Ding XJ, Zhou HM (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163CrossRef

Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B 42(2):513–529CrossRef

Iosifidis A, Tefas A, Pitas L (2013) Dynamic action recognition based on dynemes and extreme Learning Machine. Pattern Recogn Lett 34(15):1890–1898CrossRef

Kan M, Shan S, Xu D, Chen X (2011) Side-information based linear discriminant analysis for face recognition. In: Proceedings of British machine vision conference, pp 1–12

Kostinger M, Hirzer M, Wohlhart P, Roth P M, Bischof H (2012) Large scale metric learning from equivalence constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2288–2295

Lee Y, Lin Y, Wahba G (2004) Multicategory support vector machines, theory, and application to the classification of microarray data and satellite radiance data. J Am Stat Assoc 99(465):67–81MathSciNetCrossRefMATH

Li K, Kong X, Lu Z, Liu W, Yin J (2014) Boosting weighted ELM for imbalanced learning. Neurocomputing 128:15–21CrossRef

Liu X Y, Zhou Z H (2010) Learning with cost intervals. In: Proceedings of ACM SIGKDD international conference on Knowledge discovery and data mining, pp 403–412

Lu J, Tan Y P (2010) Cost-sensitive subspace learning for face recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2661–2666

Lu J, Tan YP (2013) Cost-sensitive subspace analysis and extensions for face recognition. IEEE Trans Inf Forensics Secur 8(3):510–519MathSciNetCrossRef

Martinez A, Benavente R (1998) The AR face database. CVC Technical Report 24

Mohammed AA, Minhas R, Wu QMJ, Sid-Ahmed MA (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn 44(10):2588–2597CrossRefMATH

Nguyen H V, Bai L (2010) Cosine similarity metric learning for face verification. In: Proceedings of Asian conference on computer vision, pp 709–720

Nguyen T V, Liu S, Ni B, Tan J, Rui Y, Yan S (2012) Sense beauty via face, dressing, and/or voice. In: Proceedings of ACM international conference on multimedia, pp 239–248

Nguyen T V, Liu S, Ni B, Tan J, Rui Y, Yan S (2013) Towards decrypting attractiveness via multi-modality cues. ACM Trans Multimedia Comput Commun Appl 9(4):28:1–28:20

Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRef

Yan H (2014) Cost-sensitive ordinal regression for fully automatic facial beauty assessment. Neurocomputing 129:334–342CrossRef

Yan S, Liu J, Tang X, Huang T (2007a) A parameter-free framework for general supervised subspace learning. IEEE Trans Inf Forensics Secur 2(1):69–76CrossRef

Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang H (2007b) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process 16(1):212–220MathSciNetCrossRef

Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007c) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRef

Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proceeding of international conference on computer vision, pp 543–550

Ying Y, Li P (2012) Distance metric learning with eigenvalue optimization. J Mach Learn Res 13(1):1–26MathSciNetMATH

Zhang D, Zhao Q, Chen F (2011) Quantitative analysis of human facial beauty using geometric features. Pattern Recogn 44(4):940–950CrossRef

Zhang Y, Zhou ZH (2010) Cost-sensitive face recognition. IEEE Trans Pattern Anal Mach Intell 32(10):1758–1769CrossRef

Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763CrossRefMATH

Zong W, Huang GB (2011) Face recognition based on extreme learning machine. Neurocomputting 74(16):2541–2551CrossRef

Zong W, Huang GB, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242CrossRef

Titel: Beauty Analysis by Learning Machine and Subspace Extension
verfasst von: David Zhang
Fangmei Chen
Yong Xu
Verlag: Springer International Publishing
Buch: Computer Models for Facial Beauty Analysis
Print ISBN: 978-3-319-32596-5

Electronic ISBN: 978-3-319-32598-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-32598-9_10

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner