Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Abstract: Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work

Real-Time Monitoring and Assessment System with Facial Landmark Estimation for Emotional Recognition in Work

The Model for Monitoring and Regulating Emotional States in the Work Environment based on Neural Networks and Emotion Recognition Algorithms presents an innovative approach to enhancing employee well-being and productivity by leveraging advanced technologies. This paper on the development of a system that utilizes neural networks and emotion recognition algorithms to monitor and interpret emotional cues exhibited by individuals in real-time within the work environment. With the uses of novel Directional Marker Controlled Facial Landmark (DMCFL) Emotion recognition algorithms are employed to analyze facial expressions, speech patterns, physiological data, and text-based communication to infer the emotional state of employees. Neural networks are then utilized to process this data and provide more sophisticated emotion classification and prediction. The emotional states are classified with the integrated Regression Logistics Classifier (RLC) model for classification. The analysis of the findings expressed that the real-time monitoring enables employers and supervisors to gain insights into the emotional well-being of employees, identifying patterns and potential issues. The system facilitates feedback and regulation mechanisms, allowing for personalized interventions and support tailored to individual emotional needs

Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures

Enhancing Facial Emotion Recognition with a Modified Deep Convolutional Neural Network

Understanding and predicting human character traits play a crucial role in various domains ranging from psychology to human resources. With the advent of artificial intelligence (AI) and deep learning algorithms, researchers have explored the potential of analyzing facial images to predict human character traits accurately. In this paper, we present a comprehensive study of the application of AI techniques for human character recognition. We review the existing literature on facial image analysis, AI algorithms, and personality prediction. Furthermore, we propose a methodology that leverages deep learning and convolutional neural networks (CNNs) to extract meaningful features from facial images. Our experiments demonstrate the effectiveness of our approach in accurately predicting character traits and showcasing promising results using small-scale datasets. We discuss the implications of our findings in psychology, human resources, and personalized user experiences. Additionally, ethical considerations, such as privacy and bias, are addressed. This research contributes to the growing field of AI-driven character recognition, providing insights for further advancements and practical applications in understanding human behavio