A novel facial attractiveness evaluation system based on face shape, facial structure features and skin

Truth Lies in the Depths: Novel Insights into Facial Aesthetic Measurements from a U.S. Survey Panel

BACKGROUND

Aesthetic facial bone surgery and facial implantology expand the boundaries of conventional facial surgery that focus on facial soft tissue. This study aimed to reveal novel aesthetic facial measurements to provide tailored treatment concepts and advance patient care.

METHODS

A total of n=101 study participants (46 females and 55 males) were presented with 120 patient portraits (frontal images in natural head posture; 60 females and 60 males) and asked to assess the facial attractiveness (scale 0-10; "How attractive do you find the person in the image?") and the model capability score (MCS; scale 0-10; "How likely do you think the person in the image could pursue a modelling career?"). For each frontal photograph, defined facial measurements and ratios were taken to analyse their relationship with the perception of facial attractiveness and MCS.

RESULTS

The overall attractiveness rating was 4.3 ± 1.1, while the mean MCS was 3.4 ± 1.1. In young males, there was a significant correlation between attractiveness and the zygoma-mandible angle (ZMA)2 (r= - 0.553; p= 0.011). In young and middle-aged females, MCS was significantly correlated with facial width (FW)1-FW2 ratio (r= 0.475; p= 0.034). For all male individuals, a ZMA1 value of 171.79 degrees (Y= 0.313; p= 0.024) was the most robust cut-off to determine facial attractiveness. The majority of human evaluators (n=62; 51.7%) considered facial implants a potential treatment to improve the patient's facial attractiveness.

CONCLUSION

This study introduced novel metrics of facial attractiveness, focusing on the facial skeleton. Our findings emphasized the significance of zygomatic measurements and mandibular projections for facial aesthetics, with FI representing a promising surgical approach to optimize facial aesthetics.

LEVEL OF EVIDENCE IV

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Facial expression recognition on partially occluded faces using component based ensemble stacked CNN

Facial Expression Recognition (FER) is the basis for many applications including human-computer interaction and surveillance. While developing such applications, it is imperative to understand human emotions for better interaction with machines. Among many FER models developed so far, Ensemble Stacked Convolution Neural Networks (ES-CNN) showed an empirical impact in improving the performance of FER on static images. However, the existing ES-CNN based FER models trained with features extracted from the entire face, are unable to address the issues of ambient parameters such as pose, illumination, occlusions. To mitigate the problem of reduced performance of ES-CNN on partially occluded faces, a Component based ES-CNN (CES-CNN) is proposed. CES-CNN applies ES-CNN on action units of individual face components such as eyes, eyebrows, nose, cheek, mouth, and glabella as one subnet of the network. Max-Voting based ensemble classifier is used to ensemble the decisions of the subnets in order to obtain the optimized recognition accuracy. The proposed CES-CNN is validated by conducting experiments on benchmark datasets and the performance is compared with the state-of-the-art models. It is observed from the experimental results that the proposed model has a significant enhancement in the recognition accuracy compared to the existing models.

Your face looks the same as before, only prettier: The facial skin homogeneity effects on face change detection and facial attractiveness perception

Previous studies suggested that (1) facial attractiveness perception can be increased with facial skin homogeneity improving; and (2) human's facial change detection increases along with facial skin homogeneity increases. However, it's unknown whether a face can be perceived prettier than it did before while still being considered as physically the same. It is possible that these two kinds of cognitive-aesthetic processing may have separate mathematical functions in psychophysical studies. In other words, human's facial attractiveness differentiation may be more sensitive than facial change detection. In this current study, we explored the above questions. Using three types of psychophysical techniques to manipulate facial skin homogeneity, we measured how participants' sensitivity to facial skin homogeneity and attractiveness change. Results showed a linear function curve for facial physical change detection and a logarithmic function curve was drawn in the forced-choice technique, which was the most sensitive one, indicating that participants can judge a face prettier than before without being aware of it has physically changed. Besides, two linear function curves were shown in the same/different technique and a rating technique. Taken together, this current study revealed that facial attractiveness can be enhanced and discriminated by improving facial skin homogeneity, without being realized by people with conscious awareness that the face has been changed.

Cognitive psychology-based artificial intelligence review

Most of the current development of artificial intelligence is based on brain cognition, however, this replication of biology cannot simulate the subjective emotional and mental state changes of human beings. Due to the imperfections of existing artificial intelligence, this manuscript summarizes and clarifies that artificial intelligence system combined with cognitive psychology is the research direction of artificial intelligence. It aims to promote the development of artificial intelligence and give computers human advanced cognitive abilities, so that computers can recognize emotions, understand human feelings, and eventually achieve dialog and empathy with humans and other artificial intelligence. This paper emphasizes the development potential and importance of artificial intelligence to understand, possess and discriminate human mental states, and argues its application value with three typical application examples of human–computer interaction: face attraction, affective computing, and music emotion, which is conducive to the further and higher level of artificial intelligence research.

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.