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14 - Automatic Analysis of Aesthetics: Human Beauty, Attractiveness, and Likability

from Part II - Machine Analysis of Social Signals

Published online by Cambridge University Press:  13 July 2017

By
Hatice Gunes  and
Björn Schüller
Edited by
Judee K. Burgoon ,
Nadia Magnenat-Thalmann ,
Maja Pantic  and
Alessandro Vinciarelli
Hatice Gunes
Affiliation:
University of Cambridge
Björn Schüller
Affiliation:
Imperial College London and Technical University Munich
Judee K. Burgoon
Affiliation:
University of Arizona
Nadia Magnenat-Thalmann
Affiliation:
Université de Genève
Maja Pantic
Affiliation:
Imperial College London
Alessandro Vinciarelli
Affiliation:
University of Glasgow
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Summary

According to the Oxford English Dictionary the definition of aesthetics is “concerned with beauty or the appreciation of beauty.” Despite the continuous interest and extensive research in cognitive, evolutionary, and social sciences, modeling and analysis of aesthetic canons remain open.

Contemporary theories of aesthetics emphasize critical thinking about objects, things, and people as well as experience, interaction, and value. In this regard, aesthetic norms have become more relevant to the context of interaction between humans and objects, human and computers (human–computer interaction or HCI), and between humans themselves (human–human interaction or HHI) (Kelly, 2013).

When interested readers look up the phrases aesthetics and computing on the web, they will likely encounter three main areas that appear to be related: aesthetic computing (note the missing “s” at the end), aesthetics in human–computer interaction, and computational aesthetics. Although there appears to be a close link between these three, they refer to inherently different fields of research. Aesthetic computing can be broadly defined as “applying the philosophical area of aesthetics to the field of computing” linked principally to formal languages and design of programs or products (Fishwick, 2013). Driven by design concerns, aesthetics in HCI focuses on the question of how to make computational artifacts more aesthetically pleasing (Norman, 2004). This concern has recently shifted toward aesthetics of interaction, moving the focus from ease of use to enjoyable and emotionally rewarding experience (Ahmed, Mahmud, & Bergaust, 2009). Although this question has significant theoretical and practical implications, there exists another relevant, yet largely unexplored question of whether computational approaches can be useful in understanding aesthetic judgment and affect in the context of HHI and HCI mainly given its highly subjective nature and often highly different “taste” and perception. Computational aesthetics is the research of computational methods that can make applicable aesthetic decisions in a similar way to humans (Hoeing, 2005). In other words, can human aesthetic perception and judgment be quantified computationally, and can we make machines and systems aware of aesthetics similarly to humans?

Type
Chapter
Information
Social Signal Processing , pp. 183 - 201
Publisher: Cambridge University Press
Print publication year: 2017

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References

Aarabi, P., Hughes, D., Mohajer, K., & Emami, M. (2001). The automatic measurement of facial beauty. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 4, 2644–2647.CrossRefGoogle Scholar
Ahmed, S., Al Mahmud, A., & Bergaust, K. (2009). Aesthetics in human-computer interaction: Views and reviews. In Proceedings of the 13th International Human–Computer Interaction, July 19–24, San Diego, CA.CrossRef
Alley, T. R. & Cunningham, M. R. (1991). Averaged faces are attractive, but very attractive faces are not average. Psychological Science, 2, 123–125.CrossRefGoogle Scholar
Altwaijry, Hani & Belongie, Serge (2013). Relative ranking of facial attractiveness. In Workshop on the Applications of Computer Vision (WACV), January 15–17, Clearwater Beach, FL.CrossRef
Apple, W., Streeter, L. A., & Kraus, R. M. (1979). Effects of pitch and speech rate on personal attributions. Journal of Personality and Social Psychology, 37(5), 715–727.CrossRefGoogle Scholar
Aronson, E.,Wilson, T., & Akert, R. M. (2009). Social Psychology (7th edn). Upper Saddle River, NJ: Prentice Hall.
Attabi, Y. & Dumouchel, P. (2012). Anchor models and WCCN normalization for speaker trait classification. In Proceedings of Interspeech 2012 (pp. 522–525).
Borissavlievitch, M. (1958). The Golden Number and the Scientific Aesthetics of Architecture. London: A. Tiranti.
Bottino, A. & Laurentini, A. (2010). The analysis of facial beauty: an emerging area of research in pattern analysis. In Proceedings of ICIAR 2010, 7th International Conference on Image Analysis and Recognition (pp. 425–435), June 21–23, Povoa de Varzim, Portugal.CrossRef
Brueckner, R. & Schuller, B. (2012). Likability classification – a not so deep neural network approach. In Proceedings of Interspeech, September, Portland, OR.
Buisman, H. & Postma, E. (2012). The Log-Gabor method: Speech classification using spectrogram image analysis. In Proceedings of Interspeech, September, Portland, OR.
Burkhardt, F., Eckert, M., Johannsen, W., & Stegmann, J. (2010). A database of age and gender annotated telephone speech. In LREC 2010, 7th International Conference of Language Resources and Evaluation, May 19–21, 2010, Malta.
Burkhardt, F., Schuller, B., Weiss, B., & Weninger, F. (2011). “Would you buy a car from me?” – on the likability of telephone voices. In Proceedings of the Annual Conference of INTERSPEECH (pp. 1557–1560), August 27–31, Florence, Italy.
Chan, Yin-Tzu, Hsu, Hao-Chen, Li, Po-Yi, & Yeh, Mei-Chen. (2012). Automatic cinemagraphs for ranking beautiful scenes. In Proceedings of ACM Multimedia (pp 1361–1362).CrossRef
Chattopadhyay, A., Dahl, D. W., Ritchie, R. J. B., & Shahin, K. N. (2003). Hearing voices: The impact of announcer speech characteristics on consumer response to broadcast advertising. Journal of Consumer Psychology, 13(3), 198–204.CrossRefGoogle Scholar
Chen, F. & Zhang, D. (2010). A benchmark for geometric facial beauty study. Lecture Notes in Computer Science, 6165, 21–32.CrossRefGoogle Scholar
Collins, S. A. (2000). Men's voices and women's choices. Animal Behaviour, 60, 773–780.CrossRefGoogle Scholar
Collins, S. A. & Missing, C. 2003. Vocal and visual attractiveness are related in women. Animal Behaviour, 65(5), 997–1004.Google Scholar
Cummins, N., Epps, J., & Kua, J. M. K. (2012). A comparison of classification paradigms for speaker likeability determination. In Proceedings of Interspeech, September, Portland, OR.
Cunningham, M. R., Roberts, A. R., Barbee, A. P., et al. (1995). Their ideas of beauty are, on the whole, the same as ours. Journal of Personality and Social Psychology, 68, 261–279.CrossRefGoogle Scholar
Dahlbäck, N.,Wang, Q.-Y., Nass, C., & Alwin, J. (2007). Similarity is more important than expertise: Accent effects in speech interfaces. In Proceedings of CHI 2007 – Conference on Human Factors in Computing Systems (pp. 1553–1556), April 28–May 3, San José, CA.CrossRef
Davis, B. C. & Lazebnik, S. (2008). Analysis of human attractiveness using manifold kernel regression. In Proceedings of the International Conference on Image Processing (pp. 109– 112).CrossRef
Eisenthal, Y., Dror, G., & Ruppin, E. (2006). Facial attractiveness: Beauty and the machine. Neural Computation, 18, 119–142.CrossRefGoogle Scholar
Fan, J., Chau, K. P., Wan, X., Zhai, L., & Lau, E. (2012). Prediction of facial attractiveness from facial proportions. Pattern Recognition, 45, 2326–2334.CrossRefGoogle Scholar
Farkas, L. G., Hreczko, T. A., Kolar, J. C., & Munro, I. R. (1985). Vertical and horizontal proportions of the face in young adult North American caucasians. Plastic and Reconstructive Surgery, 75, 328–338.CrossRefGoogle Scholar
Farkas, L. G. & Kolar, J. C. (1987). Anthropometrics and art in the aesthetics of women's faces. Clinics in Plastic Surgery, 14, 599–616.Google Scholar
Feinberg, D. R., DeBruine, L. M., Jones, B. C., & Perrett, D. I. (2008). The role of femininity and averageness of voice pitch in aesthetic judgments of women?s voices. Perception, 37(4), 615–623.CrossRefGoogle Scholar
Feinberg, D. R., Jones, B. C., Little, A. C., Burt, D. M., & Perrett, D. I. (2005). Manipulations of fundamental and formant frequencies influence the attractiveness of human male voices. Animal Behaviour, 69(3), 561–568.CrossRefGoogle Scholar
Fink, B., Grammer, K., & Matts, P. J. (2006). Visible skin color distribution plays a role in the perception of age, attractiveness, and health in female faces. Evolution and Human Behavior, 27, 433–442.CrossRefGoogle Scholar
Fink, B., Grammer, K., & Thornhill, R. (2001). Human (Homo sapiens) facial attractiveness in relation to skin texture and color. Journal of Comparative Psychology, 115, 92–99.CrossRefGoogle Scholar
Fishwick, P. A. (2013). Aesthetic computing. In Mads, Soegaard, & Rikke Friis, Dam (Eds), The Encyclopedia of Human–Computer Interaction (2nd edn). Aarhus, Denmark: Interaction Design Foundation.
Grimm, M. & Kroschel, K. (2005). Evaluation of natural emotions using self-assessment manikins. In Proceedings of ASRU 2005 – Automatic Speech Recognition and Understanding Workshop (pp. 381–385).CrossRef
Gunes, H. (2011). A survey of perception and computation of human beauty. In Proceedings of ACM Multimedia International Workshop on Social Signal Processing (pp. 19–24).CrossRef
Gunes, H. & Pantic, M. (2010). Automatic, Dimensional and Continuous Emotion Recognition. International Journal of Synthetic Emotions, 1(1), 68–99.CrossRefGoogle Scholar
Gunes, H. & Piccardi, M. (2006). Assessing facial beauty through proportion analysis by image processing and supervised learning. International Journal of Human–Computer Studies, 64(12), 1184–1199.CrossRefGoogle Scholar
Gunes, H. & Schuller, B. (2013). Categorical and dimensional affect analysis in continuous input: Current trends and future directions. Image & Vision Computing, 31(2), 120–136.CrossRefGoogle Scholar
Hewlett Sanchez, M., Lawson, A., Vergyri, D., & Bratt, H. (2012). Multi-system fusion of extended context prosodic and cepstral features. In Proceedings of Interspeech, September, Portland, OR.
Hodges-Simeon, C. R., Gaulin, S. J. C., & Puts, D. A. (2010). Different vocal parameters predict perceptions of dominance and attractiveness. Human Nature, 21(4), 406–427.CrossRefGoogle Scholar
Hoeing, F. (2005). Defining computational aesthetics. In Proceedings of Computational Aesthetics in Graphics, Visualization and Imaging.
Hughes, S. M., Dispenza, F., & Gallup, G. G. (2004). Ratings of voice attractiveness predict sexual behavior and body configuration. Evolution and Human Behavior, 25, 295–304.CrossRefGoogle Scholar
Huntley, H. E. (1970). The Divine Proportion: A Study in Mathematical Beauty. NewYork: Dover Publications.
Jefferson, Y. (1993). Facial aesthetics-presentation of an ideal face. Journal of General Orthodontics, 4, 18–23.Google Scholar
Ji, H. I., Kamachi, M., & Akamatsu, S. (2004). Analyses of facial attractiveness on feminised and juvenilised faces. Perception, 33, 135–145.CrossRefGoogle Scholar
Joshi, J., Gunes, H., & Goecke, R. (2014). Automatic prediction of perceived traits using visual cues under varied situational context. In Proceedings of 22nd International Conference on Pattern Recognition (ICPR).CrossRef
Jürgens, C., Johannsen, W., & Fellbaum, K. (1996). Zur Eignung von Sprechern für die Lautelemente-Bibliothek eines Sprachsynthesesystems. In: Proceedings of ITG Fachtagung Sprachkommunikation, September 17–18, Frankfurt am Main, Germany.
Kagian, A., Dror, G., Leyvand, T., Cohen-Or, D., & Ruppin, E. (2008a). A Humanlike predictor of facial attractiveness. Advances in Neural Information Processing Systems, 19, 674–683.Google Scholar
Kagian, A., Dror, G., Leyvand, T., et al. (2008b). A machine learning predictor of facial attractiveness revealing human-like psychophysical biases. Vision Research, 48, 235–243.Google Scholar
Kalayci, S., Ekenel, H. K., & Gunes, H. (2014). Automatic analysis of facial attractiveness from video. In Proceedings of IEEE International Conference on Image Processing (ICIP).CrossRef
Kelly, M. (2013). Commentary on: Fishwick, Paul A. (2013): Aesthetic Computing. In: Soegaard, Mads, & Dam, Rikke Friis (eds), The Encyclopedia of Human-Computer Interaction, 2nd Ed.
Kenny, D. T. & Mitchell, H. F. (2004). Visual and auditory perception of vocal beauty: Conflict or concurrence? In Proceedings of the 8th International Conference on Music Perception & Cognition (pp. 171–174).
Ketzmerick, B. (2007). Zur auditiven und apparativen Charakterisierung von Stimmen. Dresden: TUDpress.
Kim, J. & Moon, J. Y. (1998). Designing towards emotional usability in customer interfaces– trustworthiness of cyber-banking system interfaces. Interacting with Computers, 10(1), 1–29.CrossRefGoogle Scholar
Langlois, J. H. & Roggman, L. A. (1990). Attractive faces are only average. Psychological Science, 1, 115–121.CrossRefGoogle Scholar
Liu, X. & Xu, Y. (2011). What makes a female voice attractive? In Proceedings of ICPhS (pp. 1274–1277).
Lu, D. & Sha, F. (2012). Predicting Likability of Speakers with Gaussian Processes. In Proceedings of Interspeech, September, Portland, OR.
Michiels, G. & Sather, A. H. (1994). Determinants of facial attractiveness in a sample of white women. International Journal of Adult Orthodontics and Orthognathic Surgery, 9, 95–103.Google Scholar
Mizumoto, Y., Deguchi, T., & Fong, K. W. C. (2009). Assessment of facial golden proportions among young Japanese women. American Journal of Orthodontics and Dentofacial Orthopedics, 136, 168–174.CrossRefGoogle Scholar
Montacié, C. & Caraty, M.-J. (2012). Pitch and intonation contribution to speakers' traits classification. In Proceedings of Interspeech, September, Portland, OR.
Nguyen, T., Liu, S., Ni, B., et al. (2012). Sense beauty via face, dressing, and/or voice. In Proceedings of ACM Multimedia (pp. 239–248). Nara, Japan.CrossRef
Norman, Donald A. (2004). Emotional Design: Why We Love (Or Hate) Everyday Things. New York: Basic Books.
Pantic, M. & Vinciarelli, A. (2009). Implicit human-centered tagging. IEEE Signal Processing Magazine, 26(6), 173–180.CrossRefGoogle Scholar
Parris, C. & Robinson, J. (1999). The bold and the beautiful according to plastic surgeons, Technical report. Dallas, TX.
Pinto-Coelho, L., Braga, D., Sales-Dias, M., & Garcia-Mateo, C. (2011). An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. In Proceedings of Interspeech (pp. 2457–2460).
Pinto-Coelho, L., Braga, D., Sales-Dias, M., & Garcia-Mateo, C. (2013). On the development of an automatic voice pleasantness classification and intensity estimation system. Computer Speech and Language, 27(1), 75–88.CrossRefGoogle Scholar
Pipitone, R. Nathan & Gallup, G. G. (2008). Women's voice attractiveness varies across the menstrual cycle. Evolution and Human Behavior, 29(4), 268–274.CrossRefGoogle Scholar
Pohjalainen, J., Kadioglu, S., & Räsänen, O. (2012). Feature selection for speaker traits. In Proceedings of Interspeech, September, Portland, OR.
Rhodes, G. & Tremewan, T. (1996). Averageness exaggeration and facial attractiveness. Psychological Science, 7, 105–115.CrossRefGoogle Scholar
Ricketts, M. D. (1982). Divine proportions in facial aesthetics. Clinics in Plastic Surgery, 9, 401– 422.Google Scholar
Riding, D., Lonsdale, D., & Brown, B. (2006). The effects of average fundamental frequency and variance of fundamental frequency on male vocal attractiveness to women. Journal of Nonverbal Behaviour, 30, 55–61.CrossRefGoogle Scholar
Saxton, T. (2005). Facial and vocal attractiveness: a developmental and cross-modality study. PhD thesis, University of Edinburgh.
Saxton, T. K., Caryl, P. G., & Roberts, S. C. (2006). Vocal and facial attractiveness judgments of children, adolescents and adults: The ontogeny of mate choice. Ethology, 112(12), 1179–1185.CrossRefGoogle Scholar
Schmid, K., Marx, D., & Samal, A. (2008). Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recognition, 41, 2710–2717.CrossRefGoogle Scholar
Schuller, B., Steidl, S., Batliner, A., et al. (2012). The INTERSPEECH 2012 Speaker Trait Challenge. In Proceedings of Interspeech 2012.
Schuller, B., Wöllmer, M., Eyben, F., Rigoll, G., & Arsić, D. (2011). Semantic speech tagging: Towards combined analysis of speaker traits. In Proceedings of AES 42nd International Conference (pp. 89–97). Ilmenau, Germany: Audio Engineering Society.
Sutic, D., Brekovic, I., Huic, R., & Jukic, I. (2010). Automatic evaluation of facial attractiveness. In Proceedings of MIPRO.
Swaddle, J. P. & Cuthill, I. C. (1995). Asymmetry and human facial attractiveness: Symmetry may not always be beautiful. Biological Sciences, 261, 111–116.CrossRefGoogle Scholar
Tovee, M. J., Maisey, D. S., Emery, J. L., & Cornelissen, P. L. (1999). Visual cues to female physical attractiveness. Proceedings: Biological Sciences, 266(1415), 211–218.Google Scholar
Tuomi, S. & Fisher, J. (1979). Characteristics of simulated sexy voice. Folia Phoniatrica, 31(4), 242–249.CrossRefGoogle Scholar
Valenzano, D. R., Mennucci, A., Tartarelli, G., & Cellerino, A. (2006). Shape analysis of female facial attractiveness. Vision Research, 46, 1282–1291.CrossRefGoogle Scholar
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009). Social signal processing: Survey of an emerging domain. Image Vision Computing, 27, 1743–1759.CrossRefGoogle Scholar
Weiss, B. & Burkhardt, F. (2010). Voice attributes affecting likability perception. In Proceedings of Interspeech (pp. 1485–1488).
Weiss, B. & Möller, S. (2011). Wahrnehmungsdimensionen von Stimme und Sprechweise. In Proceedings of ESSV 2011 – 22. Konferenz Elektronische Sprachsignalverarbeitung (pp. 261– 268), September 28–30, Aachen, Germany.
Weninger, F.,Wöllmer, M., & Schuller, B. (2011). Automatic assessment of singer traits in popular music: Gender, age, height and race. In Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011 (pp. 37–42). Miami, FL: ISMIR.
White, R., Eden, A., & Maire, M. (2004). Automatic prediction of human attractiveness. UC Berkeley CS280A Project.
Whitehill, J. & Movellan, J. R. (2008). Personalized facial attractiveness prediction. In Proceedings of IEEE FGR (pp. 1–7).CrossRef
Wu, D. (2012). Genetic algorithm based feature selection for speaker trait classification. In Proceedings of Interspeech 2012.
Zhang, P. (2009). Theorizing the relationship between affect and aesthetics in the ICT design and use context. In Proceedings of the International Conference on Information Resources Management (pp. 1–15).
Zimbler, M. S. & Ham, J. (2010). Aesthetic facial analysis. In C., Cummings & P., Flint (Eds), Cummings Otolaryngology Cummings Head and Neck Surgery. St Louis:Mosby Elsevier.
Zuckerman, M. & Miyake, K. (1993). The attractive voice:What makes it so? Journal of Nonverbal Behaviour, 17(2), 119–135.Google Scholar
Kocinski, Krzysztof (2013). Perception of facial attractiveness from static and dynamic stimuli. Perception, 42, 163–175.CrossRefGoogle Scholar

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