Deep Learning for Facial Beauty Prediction

Abstract

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

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

“…CNNs' have displayed amazing facial comprehension results and recognition and had shown as efficient techniques for facial feature exploration [39], [40]. Facial beauty can be influenced by different qualities such as shape, color, geometric, and skin texture.…”

“…Human beings are affected by the faces they had seen in the past, and this could influence their perception of facial beauty [16]. A study was proposed by Cao et al [40] used a broad network design for FBP cases to achieve great performance. They introduced the use of an RIR (residual-in-residual) structure to the network when it passed through the gradient flow deeper to build a higher pathway where information can be transmitted.…”

Use of Advanced Artificial Intelligence in Forensic Medicine

“…From a forensic aspect, this era of digital 2D face manipulation brought deep-fake videos and images. Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics [149,150]. Any face can be used in the fake video or unlimited numbers of nearly authentic pictures including fake social media profiles can be created.…”

Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays

“…Convolutional neural networks (CNN) are multi-layer architectures where the successive layers are designed to progressively learn high-level features, being the last layer responsible for producing a result [22]. They have been shown to be extremely accurate for time series analysis and image classification [39,40]. The convolutional layers present in CNN are responsible for applying filters throughout the image and thus reduce its complexity.…”

“…This paper presented a novel study and application of the convolutional support vector machines to classify patients infected with COVID-19 using X-ray data. The result was compared with the use of a CNN approach for this task [36,38,39], which is considered the state-of-art in many image classification tasks [68][69][70][71]. The result showed that the CSVM outperformed the CNN approach through higher values of ACC, F1 Score, and MCC obtained in a holdout repetition as a robust validation procedure.…”