Beautiful People in the Brain of the Beholder

In the paper discussed in this chapter, the authors were interested in the neural underpinnings for facial beauty and whether such responses were automatic. In a functional magnetic resonance imaging study over two sessions, the authors asked participants to make beauty and identity judgments on a series of computer-generated faces. When people judged beauty, the authors found that neural activity varied parametrically to the degree of facial attractiveness in the fusiform face area and the lateral occipital complex, as well as in parts of parietal and frontal cortices. When people made familiarity judgments, the authors observed the same modulation of neural activity within occipital cortex to the degree of attractiveness in the faces. The data suggested that human brains automatically respond to facial beauty even when people might be attending to other aspects of the faces they apprehend.

Facial Beauty and the Medial Orbitofrontal Cortex

Faces are a highly privileged class of stimuli in humans, and facial attractiveness is a particularly salient attribute of faces that can exert considerable influence on the behavior of others. In the 2003 paper discussed in this chapter, the authors aimed to investigate the neural correlates of facial attractiveness in the brain, hypothesizing that attractive faces would recruit basic reward circuits, especially the orbitofrontal cortex. Consistent with their hypothesis, they found robust engagement of the orbitofrontal cortex to attractive faces and, moreover, that this response was enhanced if a face exhibited a smiling expression. Taken together, these results suggest that facial attractiveness and cues signaling positive social feedback can robustly recruit the brain’s reward circuitry, positioning attractive faces alongside other basic rewards while also aligning attractive faces with other aesthetically pleasing stimuli that engage similar circuits such as works of art.

Facial Beauty Prediction From Facial Parts Using Multi-Task and Multi-Stream Convolutional Neural Networks

Automatic analysis of facial beauty has become an emerging computer vision problem in recent years. Facial beauty prediction (FBP) aims at developing a human-like model that automatically makes facial attractiveness predictions. In this study, we present and evaluate a face attractiveness prediction approach using facial parts as well as a multi-task learning scheme. First, a deep convolutional neural network (CNN) pre-trained on massive face datasets is utilized for face attractiveness prediction, which is capable of automatic learning of high-level face representations. Next, we extend our deep model to other facial attribute recognition tasks. Hence, a multi-task learning scheme is leveraged by our deep model to learn optimal shared features for three correlated tasks (i.e. facial beauty assessment, gender recognition as well as ethnicity identification). To further enhance the attractiveness computation accuracy, specific regions of face images (i.e. left eye, nose and mouth) as well as the whole face are fed into multi-stream CNNs (i.e. three two-stream networks). Each two-stream network adopts a facial part as well as the full face as input. Extensive experiments are conducted on the SCUT-FBP5500 benchmark dataset, where our approach indicates significant improvement in accuracy over the other state-of-the-art methods.

Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors

The beauty industry has seen rapid growth in multiple countries and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used in the assessment of facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty. Here, the facial landmarks are extracted to calculate facial ratios according to Golden Ratios and Symmetry Ratios, and an ablation study is performed to find the best performing feature set from extracted ratios. Subsequently, Gray Level Covariance Matrix (GLCM), Hu’s Moments, and Color Histograms in the HSV space are extracted as texture, shape, and color features, respectively. Another ablation study is performed to find out which feature performs the best when concatenated with the facial landmarks. Experimental results show that the concatenation of primary facial characteristics with facial landmarks improved the prediction score of facial beauty. Four models are trained, K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) on a dataset of 5500 frontal facial images, and amongst them, KNN performs the best for the concatenated features achieving a Pearson’s Correlation Coefficient of 0.7836 and a Mean Squared Error of 0.0963. Our analysis also provides us with insights into how different machine learning models can understand the concept of facial beauty.

From “pretty to pretty powerful”: The communicatively constituted power of facial beauty’s performativity

The face is a significant locus of power upon which judgements concerning a person’s status, worth and attractiveness are made. This study contributes to knowledge of facial norms’ shifting performative power in daily organizing, theorizing facial beauty as a communicatively constituted authoritative text. We achieve this through blending Butlerian and communication as constitutive of organization (CCO) theorizing. This allows us to enrich understandings of power and performativity’s necessarily entangled and co-constitutive unfolding, as we trace how a normative understanding of facial beauty becomes more and/or less performatively powerful through embodied-textual processes. Our theorizing is generated from an ethnography of a UK cosmetics firm and demonstrates how facial beauty functions as a (figurative) authoritative text that corporealizes, subjectivizes, and is resisted by makeup artists within a confluence of (concrete) text and conversation. We show how through communicative, citational and embodied processes of corporealization, regulation and subjection, everyday performances like makeup applications become performatively powerful on the ground level of interaction. Further, returning authoritative texts to their original figurative formulation uncovers something of how their transformative power shapes organizing’s ongoing accomplishment.

The Neural Determinants of Beauty

What are the conditions that determine whether the medial orbito-frontal cortex (mOFC), in which activity correlates with the experience of beauty derived from different sources, becomes co-active with sensory areas of the brain during the experience of sensory beauty? We addressed this question by studying the neural determinants of facial beauty. The perception of faces correlates with activity in a number of brain areas, but only when a face is perceived as beautiful is the mOFC also engaged. The enquiry thus revolved around the question of whether a particular pattern of activity, within or between areas implicated in face perception, emerges when a face is perceived as beautiful, and which determines that there is, as a correlate, activity in mOFC. 17 subjects of both genders viewed and rated facial stimuli according to how beautiful they perceived them to be while the activity in their brains was imaged with functional magnetic resonance imaging (fMRI). A univariate analysis revealed parametrically scaled activity within several areas in which the strength of activity correlated with the declared intensity of the aesthetic experience of faces; the list included the mOFC and two core areas strongly implicated in the perception of faces - the occipital face area (OFA), fusiform face area (FFA)- and, additionally, the cuneus. Multivariate analyses, which reveal the more fine-grained distribution of activity in brain areas, revealed strong and distinctive patterns of activation in the FFA and the cuneus and weaker ones in the OFA and posterior superior temporal sulcus (pSTS). It is only when distinctive patterns emerged in these areas that there was co-activation of the mOFC, in which a strong pattern of activity also emerged during the experience of facial beauty. A psychophysiological interaction analysis with mOFC as the seed area revealed the involvement of the right FFA and the right OFA, but only when faces were experienced as beautiful. We conjecture that these collective patterns of activity constitute the neural basis for the experience of facial beauty, bringing us a step closer to understanding the neural determinants of aesthetic experience.