Facial Feature Detection Research Articles

Facial expression recognition based on eigenspaces and principle component analysis

Facial expression detection or emotion recognition is one of the rising fields of research on intelligent systems. Emotion plays a significant role in non-verbal communication. An efficient face and facial feature detection algorithms plays as important role in indentifying of an emotion of a person at a particular moment. In this work, the authors implemented a system that recognises a person's facial expressions from the input images, using the algorithm of eigenspaces and principle component analysis (PCA). Eigenspaces are the face images which are projected onto a feature space that encodes the variation among known face images. PCA is used in this paper to make dimensional reduction of images in order to obtain a reduced representation of face images. The implementation is applied on three different facial expressions databases, extended Cohn-Kanade facial expression database, Japanese female facial expression database and self-made database in order to find out the effectiveness of the proposed method.

A survey: facial micro-expression recognition

Facial expression recognition plays a crucial role in a wide range of applications of psychotherapy, security systems, marketing, commerce and much more. Detecting a macro-expression, which is a direct representation of an ‘emotion,’ is a relatively straight-forward task. Playing a pivotal role as macro-expressions, micro-expressions are more accurate indicators of a train of thought or even subtle, passive or involuntary thoughts. Compared to macro-expressions, identifying micro-expressions is a much more challenging research question because their time spans are narrowed down to a fraction of a second, and can only be defined using a broader classification scale. This paper is an all-inclusive survey-cum-analysis of the various micro-expression recognition techniques. We analyze the general framework for micro-expression recognition system by decomposing the pipeline into fundamental components, namely face detecting, pre-processing, facial feature detection and extraction, datasets, and classification. We discuss the role of these elements and highlight the models and new trends that are followed in their design. Moreover, we provide an extensive analysis of micro-expression recognition systems by comparing their performance. We also discuss the new deep learning features that can, in the near future, replace the hand-crafted features for facial micro-expression recognition. This survey has been developed, focusing on the methodologies applied, databases used, performance regarding recognition accuracy and comparing these to distil the gaps in the efficiencies, future scope, and research potentials. Through this survey, we intend to look into this problem and develop a comprehensive and efficient recognition scheme. This study allows us to identify open issues and to determine future directions for designing real-world micro-expression recognition systems.

Automated facial recognition of manually generated clay facial approximations: Potential application in unidentified persons data repositories

This research examined how accurately 2D images (i.e., photographs) of 3D clay facial approximations were matched to corresponding photographs of the approximated individuals using an objective automated facial recognition system. Irrespective of search filter (i.e., blind, sex, or ancestry) or rank class (R1, R10, R25, and R50) employed, few operationally informative results were observed. In only a single instance of 48 potential match opportunities was a clay approximation matched to a corresponding life photograph within the top 50 images (R50) of a candidate list, even with relatively small gallery sizes created from the application of search filters (e.g., sex or ancestry search restrictions). Increasing the candidate lists to include the top 100 images (R100) resulted in only two additional instances of correct match. Although other untested variables (e.g., approximation method, 2D photographic process, and practitioner skill level) may have impacted the observed results, this study suggests that 2D images of manually generated clay approximations are not readily matched to life photos by automated facial recognition systems. Further investigation is necessary in order to identify the underlying cause(s), if any, of the poor recognition results observed in this study (e.g., potential inferior facial feature detection and extraction). Additional inquiry exploring prospective remedial measures (e.g., stronger feature differentiation) is also warranted, particularly given the prominent use of clay approximations in unidentified persons casework.

A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH AN APPLICATION PERSPECTIVE

Image Segmentation is an important part of Image Processing. The first step in Image Analysis and Pattern Recognition is Image Segmentation. The process of Image Segmentation refers to thepartition of an image into different regions to gain information about the objects in the image based on the homogeneity or discontinuity of certain characteristics like color, intensity or texture. The purpose of this paper is to provide an overview of various image segmentation methods and their implementation in different color spaces. Their relative efficiency, advantages, and the problems they encounter are discussed. In order to understand how these methods are used, someapplications like X-Radiography, Foreground detection in MRI and Facial feature detection are explained. It is observed that no image segmentation technique and algorithm work well with all types of images but these techniques work well specific to certain applications. Hence, it is critical to decide which method is the most suitable for a particular application based on its efficiency in that domain.

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

A robust incremental clustering-based facial feature tracking

Emerging significance of person-independent, emotion specific facial feature tracking has been actively tracked in the machine vision society for decades. Among distinct methods, the Constrained Local Model (CLM) has shown significant results in person-independent feature tracking. In this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. A novel tracking system along with 17-point feature model on the frontal face region has also been proposed to facilitate the tracking of human basic facial expressions. The proposed feature tracking system keeps patch images and face shapes till certain number of key frames incorporating CLM-based tracker. After that, incremental patch and shape clustering algorithms is applied to build appearance model and structure model of similar patches and similar shapes respectively. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed Robust Incremental Clustering-based Facial Feature Tracking (RICFFT) is evaluated on the FGnet database and the Extended Cohn-Kanade (CK+) database. RICFFT demonstrates mean tracking accuracy of 97.45% and 96.64% for FGnet and CK+ database respectively. Also, RICFFT is more robust by minimizing average shape distortion error of 0.20% and 1.86% for FGnet and CK+ (apex frame) database, as compared with classic method CLM.

Combined Use of Rear Touch Gestures and Facial Feature Detection to Achieve Single-Handed Navigation of Mobile Devices

To improve single-handed operation of mobile devices, rear touch panel operation to control commands and facial feature detection to control cursor position are proposed. Operational control is achieved through finger chord gestures on a rear touch panel, and nose movement is used to control cursor movement. Zooming is achieved by detecting the apparent distance between the left and right eyes in conjunction with a finger chord gesture. We have evaluated movement time, error rates, and the throughputs of these techniques in comparison with the conventional single-handed front touch panel thumb operations using Fitts's law. Experiments have been conducted to evaluate two operation modes, selection and zooming, in the form of reciprocal 1-D pointing tasks with 12 participants. For the target selection task, the proposed technique achieved 12% (0.26 s) shorter movement time and 4.7% smaller error rate than the conventional method on average. Especially, for long distance targets, the performance of the conventional method became remarkably inferior due to the limit of reach of the thumb, whereas the proposed technique achieved much less deterioration and obtained expected performance because the cursor could reach anywhere on the display. For the target size adjustment task, the proposed technique achieved 9% (0.22 s) shorter movement time than the conventional method and obtained a comparable error rate of less than 4%. Consequently, we could demonstrate the techniques that make single-handed select and zoom operations available anywhere on a large-sized tablet device with no blockage of the display.

Incremental Clustering-Based Facial Feature Tracking Using Bayesian ART

Person-independent, emotion specific facial feature tracking have been of interest in the machine vision society for decades. Among various methods, the constrained local model (CLM) has shown significant results in person-independent feature tracking. In 63this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. Considering a 17-point feature model on the frontal face region, the proposed tracking framework incorporates CLM with two incremental clustering algorithms to increase robustness and minimize tracking error during feature tracking. The Patch Clustering algorithm is applied to build an appearance model of face frames by organizing previously encountered similar patches into clusters while the shape Clustering algorithm is applied to build a structure model of face shapes by organizing previously encountered similar shapes into clusters followed by Bayesian adaptive resonance theory (ART). Both models are used to explore the similar features/shapes in the successive images. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed incremental clustering-based facial feature tracking (ICFFT) is evaluated using the FGnet database and the extended Cohn-Kanade (CK+) database. ICFFT demonstrates better results than baseline-method CLM and provides robust tracking as well as improved localization accuracy of emotion specific facial features tracking.

Robust face alignment and tracking by combining local search and global fitting

When a face in an image is considerably occluded, existing local search and global fitting methods often cannot find the facial features due to failures in the local facial feature detectors or the fitting limitations of appearance modeling. To solve these problems, we propose a new face alignment method that combines the local search and global fitting methods, where local misalignments in the local search method are restricted by holistic appearance fitting in the global fitting method and the divergent or shrinking alignments in the global fitting method are avoided by the restricting local movements in the local search method. The proposed alignment method consists of two stages: the initialization stage detects the face, estimates the facial pose and obtains the initial facial features by locating a pose-specific mean shape on the detected face; the optimization stage then obtains the facial features by updating the parameter set from the combined Hessian matrix and the combined gradient vector. We also extend the proposed face alignment to face tracking by adding a template image that is warped from the facial features obtained in the previous frame. In the experiments, the proposed method yields more accurate and stable face alignment or tracking under heavy occlusion and pose variation than the existing methods.

Texture-based Estimation of Age and Gender from Wild Conditions

The paper concerns the estimation of facial attributes—namely, age and gender—from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, this problem is addressed by making the following contributions. First, in answer to one of the key problems of age estimation research—absence of data—a unique data set of face images, labelled for age and gender is offered, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. The images in this collection are more challenging than those offered by other face-photo benchmarks. Second, a dropout-support vector machine approach is described used by this system for face attribute estimation, in order to avoid overfitting. Inorder to make classification of age using kNN more easy, texture features are extracted. Finally, a robust face alignment technique is presented, which explicitly considers the uncertainties of facial feature detectors.

Facial shape-from-shading using features detection method

The shape reconstruction from one image is an important problem in the computer vision field. It is the so-called shape from shading problem SFS. It is known to be an ill-posed problem, because each pixel has a family cone of surface normal's satisfying the image-irradiance equation. To make the shape from shading problem well-posed, several constraints were imposed on the surface of the 3D object, the model of the camera, and the light source which illuminates the 3D object. The idea of the proposed method is to use a facial features detection method to determine the singular points of the face in order to use them to generate the 3D object. It occurs in two main steps: the first step is the extraction of the facial features the eyes, the nose and the mouth. Using this information, we can extract the singular points which represent the maximum gray-scale value and apply the local fast-marching method to compute the depth of these points. The second step is the application of the global fast-marching to compute all points around the singular points. The proposed method is tested on real facial images.

A Hierarchical Regression Approach for Unconstrained Face Analysis

Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the efficiency and robustness. To eliminate noise influence of unconstrained environment, facial patches extracted from face area are classified as positive or negative facial patches, only positive facial patches are used for face analysis. The proposed hierarchical D-RF works in two iterative procedures. First, coarse head pose is estimated to constrain the facial features detection, then the head pose is updated based on the estimated facial features. Second, the facial feature localization is refined based on the updated head pose. In order to further improve the efficiency and robustness, multiple probabilitic models are learned in leaves of the D-RF, i.e. the patch’s classification, the head pose probabilities, the locations of facial points and face deformation models (FDM). Moreover, our algorithm takes a composite weight voting method, where each patch extracted from the image can directly cast a vote for the head pose or each of the facial features. Extensive experiments have been done with different publicly available databases. The experimental results demonstrate that the proposed approach is robust and efficient for head pose and facial feature detection.

Human Facial Features Detection and Tracking in Images and Video

Human Facial Features Detection and Tracking in Images and Video

A robust analysis, detection and recognition of facial features in 2.5D images

A robust technique for recognition of 3D faces which performs well with face images with various poses, expressions and occlusions. In this method, the face images represented in 3D mesh format are smoothed using trilinear interpolation and then converted to 2.5D image or range images. Nose-tip which is the most prominent feature on human face is detected first on the corner points selected by 3D Harris corner and curvedness at those corner points. K-Means clustering is applied to group those corner points in 2 groups. The cluster of points with larger curvedness values represents the possible locations of nose-tip. Nose-tip is finally localized using Mean-Gaussian curvature values of the prospective corner points in that cluster. Using the nose-tip location, other facial landmarks namely corners of the eyes and mouth are located and a facial graph is generated. The dimensionality of 2.5D feature space is that, depth values are stored at each (x, y) grid of the 2.5D image, so a 3D face image uses some function to map the depth value at any pixel position to the intensity with which that pixel will be displayed. Here finally extracted features for each subject is of dimensionality [1ź?ź21], taking into account the Euclidean distances in three dimensional form between each feature points detected automatically. Taking Euclidean distances between all pairs of landmark points as features, face images are classified using Multilayer Perceptron (MLP), as well as Support Vector Machines (SVM). Maximum recognition rates of 75 and 87.5 % have been obtained in case of Bosphorus Databases, 62.5 and 87.5 % in case of GavabDB databases, 75 and 87.5 % in case of Frav3D Databases by Multilayer Perceptron and Support Vector Machines respectively.

DETEKSI MATA REAL TIME MENGGUNAKAN OPENCV UNTUK ANDROID

Right now there has been a lot of developing applications that use the face detection feature. Facedetection itself can be done in various ways, one using the Viola-Jones method, a method thatcombines support vector machines, boosting algorithm, and a cascade classifie. In this researchdesigned an eye detection system by using a Viola-Jones method with OpenCV library. Data fromthis research are in the form of a live video capture using camera smartphone with android os

A Survey of Feature Base Methods for Human Face Detection

The human face is among the most significant objects in an image or video, it contains many important information and specifications, also is required to be the cause of almost all achievable look variants caused by changes in scale, location, orientation, pose, facial expression, lighting conditions and partial occlusions. It plays a key role in face recognition systems and many other face analysis applications. We focus on the feature based approach because it gave great results on detect the human face. Face feature detection techniques can be mainly divided into two kinds of approaches are Feature base and image base approach. Feature base approach tries to extract features and match it against the knowledge of the facial features. This paper gives the idea about challenging problems in the field of human face analysis and as such, as it has achieved a great attention over the last few years because of its many applications in various domains. Furthermore, several existing face detection approaches are analyzed and discussed and attempt to give the issues regarding key technologies of feature base methods, we had gone direct comparisons of the method's performance are made where possible and the advantages/ disadvantages of different approaches are discussed.

Identification and Tracking of Facial Features

Abstract Facial feature points such as eyebrows, eyes, nose and mouth are prominent landmarks surrounding facial components. They depicts critical information about facial expression and head movement .Hence accurate and perfect detection of facial features is important in several applications like face recognition,face tracking,criminal identification,security and surveillance systems,Human computer interaction(HCI), model based video coding. This paper represents traditional methodologies and taxonomies along with brief introduction to recent approaches and advancements in the field proposed by various researchers that acts as a bottleneck under various situations like occlusion, pose variations, different lighting conditions.

A 3D-Based Pose Invariant Face Recognition at a Distance Framework

Face recognition in the wild can be defined as recognizing individuals unabated by pose, illumination, expression, and uncertainties from the image acquisition. In this paper, we propose a framework recognizing human faces under such uncertainties by focusing on the pose problem while considering the other factors together. The proposed work introduces an automatic front-end stereo-based system, which starts with image acquisition and ends by face recognition. Once an individual is detected by one of the stereo cameras, its facial features are identified using a facial features extraction model. These features are used to steer the second camera to see the same subject. Then, a stereo pair is captured and 3D face is reconstructed. The proposed stereo matching approach carefully handles illumination variance, occlusion, and disparity discontinuity. The reconstructed 3D shape is used to synthesize virtual 2D views in novel poses. All these steps are done off-line in an Enrollment stage. To recognize a face from a 2D image, which is captured under unknown environmental conditions, another fast on-line stage starts by facial features detection. Then, a facial signature is extracted from patches around these facial features. Finally, this probe image is matched against the closest synthesized images. Experiments are conducted on different public databases from where we investigate the effect of each component of the proposed framework on the recognition performance. The results confirm that without training and with automatic features extraction, our proposed face recognition at a distance approach outperforms most of the state-of-the-art approaches.

Age and Gender Estimation of Unfiltered Faces

This paper concerns the estimation of facial attributes—namely, age and gender—from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research—absence of data—we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

Hybrid Approach for Facial Feature Detection and Tracking under Occlusion

Hybrid Approach for Facial Feature Detection and Tracking under Occlusion