Automatic Facial Feature Research Articles

Automatic facial skin feature detection for everyone

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. S Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges. The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions. The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation. We employ Unet++ as the network architecture for feature detection. This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.

Automatic facial feature points location based on deep learning: a review

Automatic facial feature points location based on deep learning: a review

Discriminating drivers’ emotions through the dimension of power: Evidence from facial infrared thermography and peripheral physiological measurements

The goal of this study was to investigate changes in body temperature as indicators for the emotional dimension of power during driving. Therefore, a driving simulator experiment with 18 participants was conducted, in which two emotions (Fear and noFear) with different characteristics in the dimension of power (low power and high power), which is described as power or weakness feelings of control, were induced using threat and challenge events in the driving scenarios. Infrared thermography video and automatic facial feature recognition were implemented to assess participants’ facial temperature. It was revealed that forehead temperature, which is supposed to represent emotional dimension of power, decreased significantly more after threat than after challenge events (t(17) = −1.83, p = 0.04, Cohen’s d = 0.54). These results suggest that forehead temperature as an indicator for the emotional dimension of power can help to measure drivers’ fear and thus aid reliable in-vehicle emotion recognition.

Facial component-based blended facial expressions generation from static neutral face images

Facial expression synthesis is getting a wide-spread attention since past several years due to its multimedia applications. In most of the earlier research works, example images of target expressions are required to produce synthesized facial expressions. The paper aims to generate six basic and twelve blended facial expressions from a static and RGB neutral face image without any exemplar of expressive face images. The proposed automatic expression generation system consists of several sub-systems, namely, a knowledge-based system, a module for symbolic formulations of basic and blended facial expressions, an expressive facial components generator and an expressive face generator. The knowledge-based system stores the normalized facial feature parameter values. Symbolic formulations of facial expressions are used to reconstruct facial expressions from a static neutral face image using the parameters stored in the knowledge base. Expressive facial components generator performs automatic expressive facial feature generation as well as automatic facial feature extraction and landmark annotation. Finally, expressive facial components are combined to produce an expressive face in expressive face generator. The system generated expressive face images are validated in four different ways: inter-rater reliability measure, similarity measurement in the frequency domain, similarity measurement using SSIM, FSIM, HOG features and accuracy measurement using both appearance-based and geometry-based feature extraction methods. The geometry based feature extraction method generates 90% recognition accuracy for system generated face images.

Automatic Facial Feature Extraction for Predicting Designers' Comfort With Engineering Equipment During Prototype Creation

Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.

Face Detection and Sex Identification from Color Images using AdaBoost with SVM based Component Classifier

When we talk about a computer based automatic facial feature extraction system which can identify face, gesture etc and estimate sex, age, expirations etc, we always ask for a dependable, fast, reliable classification process. This paper presents an approach to extract effective features for face detection and sex classification system. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions in the color image. Finally Gaussian fitted skin color model is used to obtain the likelihood of skin for any pixel of an image. For facial feature extraction we use Gabor filters at five scales and eight orientations. To solve the classification problem we employ Adaboost_SVM based classifier. Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on combination of Adaboost with Support Vector Machine (SVM) as weak component classifiers to be used in sex classification task. For this classification problem AdaboostSVM based classifier demonstrates better generalization performance than SVM on imbalanced classification problems.

Unsupervised SIFT-based Face Recognition Using an Automatic Hierarchical Agglomerative Clustering Solution

In this paper, we propose a robust automatic unsupervised face recognition system using SIFT characteristics. A SIFT- based feature extraction is performed on the analyzed face images. Then, we introduce a novel metric for the obtained feature vectors. Next, we develop an automatic facial feature vector classification technique based on a hierarchical agglomerative clustering algorithm and some validation indexes. The recognition system described here works for large sets of faces and can be successfully applied in the face database indexing domain.

Automatic Facial Feature Extraction and Expression Recognition based on Neural Network

In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

Robust Facial Feature Tracking Using Shape-Constrained Multiresolution-Selected Linear Predictors

This paper proposes a learned data-driven approach for accurate, real-time tracking of facial features using only intensity information. The task of automatic facial feature tracking is nontrivial since the face is a highly deformable object with large textural variations and motion in certain regions. Existing works attempt to address these problems by either limiting themselves to tracking feature points with strong and unique visual cues (e.g., mouth and eye corners) or by incorporating a priori information that needs to be manually designed (e.g., selecting points for a shape model). The framework proposed here largely avoids the need for such restrictions by automatically identifying the optimal visual support required for tracking a single facial feature point. This automatic identification of the visual context required for tracking allows the proposed method to potentially track any point on the face. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel intensities into tracked feature position displacements. Building upon the simplicity and strengths of linear predictors, a more robust biased linear predictor is introduced. Multiple linear predictors are then grouped into a rigid flock to further increase robustness. To improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multiresolution LP model. Finally, we also exploit a simple shape constraint for correcting the occasional tracking failure of a minority of feature points. Experimental results show that this method performs more robustly and accurately than AAMs, with minimal training examples on example sequences that range from SD quality to Youtube quality. Additionally, an analysis of the visual support consistency across different subjects is also provided.

Automatic facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

We present a fully automated algorithm for facial feature extraction and 3D face modeling from a pair of orthogonal frontal and profile view images of a person's face taken by calibrated cameras. The algorithm starts by automatically extracting corresponding 2D landmark facial features from both view images, then compute their 3D coordinates. Further, we estimate the coordinates of the features that are hidden in the profile view based on the visible features extracted in the two orthogonal face images. The 3D coordinates of the selected feature points obtained from the images are used first to align, then to locally deform the corresponding facial vertices of the generic 3D model. Preliminary experiments to assess the applicability of the resulted models for face recognition show encouraging results.

AUTOMATIC FACIAL FEATURE EXTRACTION USING EDGE DISTRIBUTION AND GENETIC SEARCH

A heuristic, automatic facial feature extraction approach is presented in this paper. The method is based on the edge density distribution of the image, containing non-occluded, nearly frontal view of one or more faces. In the preprocessing stage, a face is approximated to an ellipse, and genetic algorithm is applied to search for the best ellipse region match. In the feature extraction stage, genetic algorithm is applied to extract the facial features, such as the eyes (with eyebrows), nose and mouth, in the predefined subregions. The simulation results validates that the proposed method is capable of automatically extracting features from various video images effectively under natural lighting environments and in the presence of complex backgrounds, certain amount of artificial noise and of multi-face oriented with angles. This preliminary study advanced from a rich literature provides robust facial feature detection under certain variations in lighting conditions and backgrounds.

Automatic facial feature extraction by genetic algorithms

An automatic facial feature extraction algorithm is presented. The algorithm is composed of two main stages: the face region estimation stage and the feature extraction stage. In the face region estimation stage, a second-chance region growing method is adopted to estimate the face region of a target image. In the feature extraction stage, genetic search algorithms are applied to extract the facial feature points within the face region. It is shown by simulation results that the proposed algorithm can automatically and exactly extract facial features with limited computational complexity.

Towards a system for automatic facial feature detection

A model-based methodology is proposed to detect facial features from a front-view ID-type picture. The system is composed of three modules: context (i.e. face location), eye, and mouth. The context module is a low resolution module which defines a face template in terms of intensity valley regions. The valley regions are detected using morphological filtering and 8-connected blob coloring. The objective is to generate a list of hypothesized face locations ranked by face likelihood. The detailed analysis is left for the high resolution eye and mouth modules. The aim for both is to confirm as well as refine the locations and shapes of their respective features of interest. The detection is done via a two-step modelling approach based on the Hough transform and the deformable template technique. The results show that facial features can be located very quickly with Adequate or better fit in over 80% of the images with the proposed system.