This work presents a framework for emotion recognition, based in facial expression analysis
using Bayesian Shape Models (BSM) for facial landmarking localization. The Facial Action
Coding System (FACS) compliant facial feature tracking based on Bayesian Shape Model. The
BSM estimate the parameters of the model with an implementation of the EM algorithm. We
describe the characterization methodology from parametric model and evaluated the accuracy
for feature detection and estimation of the parameters associated with facial expressions,
analyzing its robustness in pose and local variations. Then, a methodology for emotion
characterization is introduced to perform the recognition. The experimental results show that
the proposed model can effectively detect the different facial expressions. Outperforming
conventional approaches for emotion recognition obtaining high performance results in the
estimation of emotion present in a determined subject. The model used and characterization
methodology showed efficient to detect the emotion type in 95.6% of the cases.
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...TELKOMNIKA JOURNAL
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Data Mining - Facial Expression RecognitionArshiaSali1
Automatic Facial Expression Recognition by employing motion vector features (deformation generated on a face during a facial expression) used as our data. Some of the most
state-of-the-art classification algorithms such as C5.0, CRT,
QUEST, CHAID, Deep Learning (DL), SVM and Discriminant
algorithms are used to classify the extracted motion vectors.
FACIAL EXPRESSION RECOGNITION USING DIGITALISED FACIAL FEATURES BASED ON ACTI...csandit
Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential.The recent feature extraction methods for facial expression recognition are similar to face
recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity
and extract the most useful information from the facial image. The result shows by using this
method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...TELKOMNIKA JOURNAL
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Data Mining - Facial Expression RecognitionArshiaSali1
Automatic Facial Expression Recognition by employing motion vector features (deformation generated on a face during a facial expression) used as our data. Some of the most
state-of-the-art classification algorithms such as C5.0, CRT,
QUEST, CHAID, Deep Learning (DL), SVM and Discriminant
algorithms are used to classify the extracted motion vectors.
FACIAL EXPRESSION RECOGNITION USING DIGITALISED FACIAL FEATURES BASED ON ACTI...csandit
Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential.The recent feature extraction methods for facial expression recognition are similar to face
recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity
and extract the most useful information from the facial image. The result shows by using this
method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Facial Expression Recognition Based on Facial Motion Patternsijeei-iaes
Facial expression is one of the most powerful and direct mediums embedded in human beings to communicate with other individuals’ feelings and abilities. In recent years, many surveys have been carried on facial expression analysis. With developments in machine vision and artificial intelligence, facial expression recognition is considered a key technique of the developments in computer interaction of mankind and is applied in the natural interaction between human and computer, machine vision and psycho- medical therapy. In this paper, we have developed a new method to recognize facial expressions based on discovering differences of facial expressions, and consequently appointed a unique pattern to each single expression.by analyzing the image by means of a neighboring window on it, this recognition system is locally estimated. The features are extracted as binary local features; and according to changes in points of windows, facial points get a directional motion per each facial expression. Using pointy motion of all facial expressions and stablishing a ranking system, we delete additional motion points that decrease and increase, respectively, the ranking size and strenghth. Classification is provided according to the nearest neighbor. In the conclusion of the paper, the results obtained from the experiments on tatal data of Cohn-Kanade demonstrate that our proposed algorithm, compared to previous methods (hierarchical algorithm combined with several features and morphological methods as well as geometrical algorithms), has a better performance and higher reliability.
Face detection for video summary using enhancement based fusion strategyeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The face recognition system is critical when individuals have very similar biometric signature such
as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed
according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled and rotated facial images of identical twins in different illuminations.
The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show
that the proposed method is robust to rotation, scaling and changing illumination.
AN EFFICIENT FEATURE EXTRACTION METHOD WITH LOCAL REGION ZERNIKE MOMENT FOR F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled and rotated facial images of identical twins in different illuminations. The results prove the ability of proposed method to recognize a pair of identical twins.Also, results show that the proposed method is robust to rotation, scaling and changing illumination.
SPONTANEOUS SMILE DETECTION WITH APPLICATION OF LANDMARK POINTS SUPPORTED BY ...csandit
When automatic recognition of emotion became feasible, novel challenges has evolved. One of them is the recognition whether a presented emotion is genuine or not. In this work, a fully automated system for differentiation between spontaneous and posed smiles is presented. This solution exploits information derived from landmark points, which track the movement of fiducial elements of face. Additionally, the smile intensity computed with SNiP (Smile-Neutral intensity Predictor) system is exploited to deliver additional descriptive data. The performed experiments revealed that when an image sequence describes all phases of smile, the landmark
points based approach achieves almost 80% accuracy, but when only onset is exploited,additional support from visual cues is necessary to obtain comparable outcomes.
Facial animation is an important aspect to make character more life in virtual world. Early stage to make
facial animation is facial rigging. Facial rigging is the process of creating the animation controls for a
facial model and the animator’s interface to those controls. Facial feature points which associated with
facial skeleton makes process of rigging easy to do. Weighting feature points on the face makes facial
skeleton used as the basis of the manufacture of facial control to manual or automatic animation with
motion data.
Driver’s drowsiness is one of the major causes of serious accidents in road traffic. Thus, special effort in searching for better assistant technology has been paid. However, several existing approaches fail to work effectively as the head of a drowsy driver is usually in slanting state. Moreover, the shaking of vehicle or the driver’s winking even makes the problem much more complicated. Anyway, head bend posture also signifies a drowsy state. Consequently, this paper proposes a novel approach by considering head nodding behaviour as an input in our detection model. After detecting a human face, some significant facial features are extracted; then, they are used to calculate the predetermined optimal parameters; finally, drowsiness is evaluated based on these thresholds. In our empirical experiments, the proposed algorithm can successfully and accurately detect 96.56% of cases.
Driver's drowsiness is one of the major causes of serious accidents in road traffic. Thus, special effort in searching for better assistant technology has been paid. However, several existing approaches fail to work effectively as the head of a drowsy driver is usually in slanting state. Moreover, the shaking of vehicle or the driver's winking even makes the problem much more complicated. Anyway, head bend posture also signifies a drowsy state. Consequently, this paper proposes a novel approach by considering head nodding behavior as an input in our detection model. After detecting a human face, some significant facial features are extracted; then, they are used to calculate the predetermined optimal parameters; finally, drowsiness is
evaluated based on these thresholds. In our empirical experiments, the proposed algorithm can successfully
and accurately detect 96.56% of cases.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Focus Group con Streaming para que pueda hacer el seguimiento desde cualquier lugar del mundo. Mayor comodidad, ahorre tiempo y dinero, mejore el trabajo de su equipo.
Facial Expression Recognition Based on Facial Motion Patternsijeei-iaes
Facial expression is one of the most powerful and direct mediums embedded in human beings to communicate with other individuals’ feelings and abilities. In recent years, many surveys have been carried on facial expression analysis. With developments in machine vision and artificial intelligence, facial expression recognition is considered a key technique of the developments in computer interaction of mankind and is applied in the natural interaction between human and computer, machine vision and psycho- medical therapy. In this paper, we have developed a new method to recognize facial expressions based on discovering differences of facial expressions, and consequently appointed a unique pattern to each single expression.by analyzing the image by means of a neighboring window on it, this recognition system is locally estimated. The features are extracted as binary local features; and according to changes in points of windows, facial points get a directional motion per each facial expression. Using pointy motion of all facial expressions and stablishing a ranking system, we delete additional motion points that decrease and increase, respectively, the ranking size and strenghth. Classification is provided according to the nearest neighbor. In the conclusion of the paper, the results obtained from the experiments on tatal data of Cohn-Kanade demonstrate that our proposed algorithm, compared to previous methods (hierarchical algorithm combined with several features and morphological methods as well as geometrical algorithms), has a better performance and higher reliability.
Face detection for video summary using enhancement based fusion strategyeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The face recognition system is critical when individuals have very similar biometric signature such
as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed
according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled and rotated facial images of identical twins in different illuminations.
The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show
that the proposed method is robust to rotation, scaling and changing illumination.
AN EFFICIENT FEATURE EXTRACTION METHOD WITH LOCAL REGION ZERNIKE MOMENT FOR F...ieijjournal
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, the facial area in an image is detected using AdaBoost approach. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image.The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the local regions of facial area of identical twins images. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled and rotated facial images of identical twins in different illuminations. The results prove the ability of proposed method to recognize a pair of identical twins.Also, results show that the proposed method is robust to rotation, scaling and changing illumination.
SPONTANEOUS SMILE DETECTION WITH APPLICATION OF LANDMARK POINTS SUPPORTED BY ...csandit
When automatic recognition of emotion became feasible, novel challenges has evolved. One of them is the recognition whether a presented emotion is genuine or not. In this work, a fully automated system for differentiation between spontaneous and posed smiles is presented. This solution exploits information derived from landmark points, which track the movement of fiducial elements of face. Additionally, the smile intensity computed with SNiP (Smile-Neutral intensity Predictor) system is exploited to deliver additional descriptive data. The performed experiments revealed that when an image sequence describes all phases of smile, the landmark
points based approach achieves almost 80% accuracy, but when only onset is exploited,additional support from visual cues is necessary to obtain comparable outcomes.
Facial animation is an important aspect to make character more life in virtual world. Early stage to make
facial animation is facial rigging. Facial rigging is the process of creating the animation controls for a
facial model and the animator’s interface to those controls. Facial feature points which associated with
facial skeleton makes process of rigging easy to do. Weighting feature points on the face makes facial
skeleton used as the basis of the manufacture of facial control to manual or automatic animation with
motion data.
Driver’s drowsiness is one of the major causes of serious accidents in road traffic. Thus, special effort in searching for better assistant technology has been paid. However, several existing approaches fail to work effectively as the head of a drowsy driver is usually in slanting state. Moreover, the shaking of vehicle or the driver’s winking even makes the problem much more complicated. Anyway, head bend posture also signifies a drowsy state. Consequently, this paper proposes a novel approach by considering head nodding behaviour as an input in our detection model. After detecting a human face, some significant facial features are extracted; then, they are used to calculate the predetermined optimal parameters; finally, drowsiness is evaluated based on these thresholds. In our empirical experiments, the proposed algorithm can successfully and accurately detect 96.56% of cases.
Driver's drowsiness is one of the major causes of serious accidents in road traffic. Thus, special effort in searching for better assistant technology has been paid. However, several existing approaches fail to work effectively as the head of a drowsy driver is usually in slanting state. Moreover, the shaking of vehicle or the driver's winking even makes the problem much more complicated. Anyway, head bend posture also signifies a drowsy state. Consequently, this paper proposes a novel approach by considering head nodding behavior as an input in our detection model. After detecting a human face, some significant facial features are extracted; then, they are used to calculate the predetermined optimal parameters; finally, drowsiness is
evaluated based on these thresholds. In our empirical experiments, the proposed algorithm can successfully
and accurately detect 96.56% of cases.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Focus Group con Streaming para que pueda hacer el seguimiento desde cualquier lugar del mundo. Mayor comodidad, ahorre tiempo y dinero, mejore el trabajo de su equipo.
A novel approach for performance parameter estimation of face recognition bas...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Human’s facial parts extraction to recognize facial expressionijitjournal
Real-time facial expression analysis is an important yet challenging task in human computer interaction.
This paper proposes a real-time person independent facial expression recognition system using a
geometrical feature-based approach. The face geometry is extracted using the modified active shape
model. Each part of the face geometry is effectively represented by the Census Transformation (CT) based
feature histogram. The facial expression is classified by the SVM classifier with exponential chi-square
weighted merging kernel. The proposed method was evaluated on the JAFFE database and in real-world
environment. The experimental results show that the approach yields a high recognition rate and is
applicable in real-time facial expression analysis.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Spontaneous Smile Detection with Application of Landmark Points Supported by ...cscpconf
When automatic recognition of emotion became feasible, novel challenges has evolved. One of
them is the recognition whether a presented emotion is genuine or not. In this work, a fully
automated system for differentiation between spontaneous and posed smiles is presented. This
solution exploits information derived from landmark points, which track the movement of
fiducial elements of face. Additionally, the smile intensity computed with SNiP (Smile-Neutral
intensity Predictor) system is exploited to deliver additional descriptive data. The performed
experiments revealed that when an image sequence describes all phases of smile, the landmark
points based approach achieves almost 80% accuracy, but when only onset is exploited,
additional support from visual cues is necessary to obtain comparable outcomes.
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Fiducial Point Location Algorithm for Automatic Facial Expression Recognitionijtsrd
We present an algorithm for the automatic recognition of facial features for color images of either frontal or rotated human faces. The algorithm first identifies the sub images containing each feature, afterwards, it processes them separately to extract the characteristic fiducial points. Then Calculate the Euclidean distances between the center of gravity coordinate and the annotated fiducial points coordinates of the face image. A system that performs these operations accurately and in real time would form a big step in achieving a human like interaction between man and machine. This paper surveys the past work in solving these problems. The features are looked for in down sampled images, the fiducial points are identified in the high resolution ones. Experiments indicate that our proposed method can obtain good classification accuracy. D. Malathi | A. Mathangopi | Dr. D. Rajinigirinath ""Fiducial Point Location Algorithm for Automatic Facial Expression Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21754.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/21754/fiducial-point-location-algorithm-for-automatic-facial-expression-recognition/d-malathi
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
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2. 526 Computer Science & Information Technology (CS & IT)
that developing a good methodology of emotional states characterization based on facial
expressions, leads to more robust recognition systems. Facial Action Coding System (FACS)
proposed by Ekman et al. [1], is a comprehensive and anatomically based system that is used to
measure all visually discernible facial movements in terms of atomic facial actions called Action
Units (AUs). As AUs are independent of interpretation, they can be used for any high-level
decision-making process, including the recognition of basic emotions according to Emotional
FACS (EM-FACS), the recognition of various affective states according to the FACS Affect
Interpretation Database (FACSAID) introduced by Ekman et al. [4], [5], [6], [7]. From the
detected features, it is possible to estimate the emotion present in a particular subject, based on an
analysis of estimated facial expression shape in comparision to a set of facial expressions of each
emotion [3], [8], [9].
In this work, we develop a novel technique for emotion recognition based on computer vision
techniques by analysing the visual answers of the facial expression, from the variations that
present the facial features, specially those in regions of FACS. Those features are detected by
using bayesian estimation techniques as Bayesian Shape Model (BSM) proposed on [10], which
from the a-priori object knowledge (face to be analyzed) and being help by a parametric model
(Candide3 in this case) [11], allow to estimate the object shape with a high accuracy level. The
motivation for its conception, is an extension of the Active Shape Model (ASM) and Active
Appeareance Model (AAM) which predominantly considers the shape of an object class [12]
[13]. Although facial feature detection is widely used in facial recognition tasks, databases
searching, image restoration and other models based on coding sequences of images containing
human faces. The accuracy which these features are detected does not reach high percentages of
performance due some issues present at the scene as the lighting changes, variations in pose of
the face in the scene or elements that produce some kind of occlusion. Therefore, it is important
to analyze how useful would be the image processing techniques developed for this work. The
rest of the paper is arranged as follows. Section 2 provides a detailed discussion of model-based
facial feature extraction. Section 3 presents our emotion characterization method. Sections 4 and
5 discuss the experimental setup and results respectively. The paper concludes in Section 6, with
a summary and discussion for future research.
2. A BAYESIAN FORMULATION TO SHAPE REGISTRATION
The probabilistic formulation of shape registration problem contains two models: one denotes the
prior shape distribution in tangent shape space and the other is a likelihood model in image shape
space. Based on these two models we derive the posterior distribution of model parameters.
2.1 Tangent Space Approximation
3. Computer Science & Information Technology (CS & IT) 527
Fig. 1. The mean face model and all the training sets normalized face models [16]
3. EMOTION CHARACTERIZATION
3.1 Facial Expression Analysis
Facial expressions are generated by contraction or relaxation of facial muscles or by other
physiological processes such as coloring of the skin, tears in the eyes or sweat on the skin. We
restrict ourselves to the contraction or relaxation of the muscles category. The contraction or
relaxation of muscles can be described by a system of 43 Action Units (AUs) [1]
Labeling of the Emotion Type After choosing that AUs are used to represent the facial
expressions that correspond to each emotion, we chose Candide3 vertexes used to define the
4. 528 Computer Science & Information Technology (CS & IT)
configuration of the face and the calculation of the FACS [11]. Figure 2 shows the shapes of the
Candide3 models playing each emotion.
Fig. 2. Shape of each emotion type
4. EXPERIMENTAL SETUP
In this work we used the MUCT database which consists of 3755 faces from 276 subjects with 76
manual landmarks [16].
4.1 Evaluation Metrics
The shape model will be estimated for every subject, contemplating 5 changes of pose exposure
(-350
and 350
in the axis X and (-250
and 250
) in the axis Y to simulate the nods) and 1 frontal,
with which 1124 images will be had for changes of pose exposure and 276 frontal images. The
first step is to compute the average error of the distance between the manually labeled landmark
points pi and points estimated by the model for all training and test images. We compute the
relative error between the manually labeled landmark points and landmark points estimated by
the model for the eyelids region [17].
4.2 Landmarking The Training Set
The initial step is the selection of images to incorporate into the training set. In order to decide
which images will be include in the training set, the desired variations must be considered. In
order to include the whole region of the labeled face, there will be used a set of landmarks, which
consist in a group of 76 landmark points that depict the whole face regions in detail (eyes, nose,
mouth, chin, etc.) [16].
Fig. 3. Samples of the landmarking process
5. Computer Science & Information Technology (CS & IT) 529
After estimate the landmark points of BSM, procrustes analysis is performed to align an
estimated facial expression with respect to standard shape (Neutral emotion). Then, we extract all
the combinations of the estimated vertexes model and perform combinations of these sets
(complete set, set of eyes, eyebrows and mouth).
5. RESULTS
5.1 Bayesian shape Model Estimation Error
In Table 5.1, It can be seen that although the accuracy in the estimation of the points is greater for
images of the training set, the average error is also small for the test images. This is due to a
rigorous procedure in the training and model building in which there were considered to be the
biggest quantity of possible forms to estimate. Moreover, it is noted that although the average
error for images with changes in pose is a bit higher than in the case of frontal images, this
indicates that the accuracy in estimating the model is so high for images that show changes in the
pose of the face. Also, it is of highlighting that the times average of estimation of the model
BSM, they are relatively small which would help in applications on line.
5.2 Distribution on the Relative Error
Figure 4(a) shows the distribution function of the relative error against successful detection rate,
on which it is observed that for a relative error of 0:091 in the case of the adjustment of the right
eye, 0:084 for the left eye and 0:125 for the mouth region in frontal images, the detection rate is
100%, indicating that the accuracy in the BSM model fit is high. In addition to images with pose
variations in BSM is achieved 100% for adjusting the eye region for relative errors to 0:120,
0:123 and 0:13 for the mouth region; being this much lower than the established criterion of 0:25.
It is considered that the criterion Rerr < 0:25 is not adapted to establish a detection like correct
and can be not very suitable when it is desirable to realize facial features detection in images with
minor scale. That's why it is considered to be a successful detection for errors Rerr < 0:15 [18].
6. 530 Computer Science & Information Technology (CS & IT)
Fig. 4. Shape of each emotion type.
Finally a set of fitting images by BSM model is shown in Figure 4(b) evidencing the robustness
of the proposed model.
5.3 Emotion Recognition
Table 2 shows the average success rates in the emotion detection obtained for all sets used in this
analysis using the MUCT database proving the high accuracy of the RMSE metric. Another
important factor in this parsing is the fact that the sets wich provide more evidence on the
emotional estimation state are the sets of B;C (mouth and eyebrow separated) with 95:6% of
average success rate, B;C;O (mouth, eyebrows and eyes separate set) with 92:2%, CO;B
(Eyebrow, Eyes together and Mouth separately) with 91:1%, and where the first of these provides
more discriminant results for the estimation of the emotional detection rate.
Table 2. Successful rate emotion recognition for MUCT database
.
6. CONCLUSION AND FUTURE WORKS
This paper presents a bayesian shape model for emotion recognition using facial expression
analisys. By projecting shape to tangent shape, we have built the model describing the prior
distribution of face shapes and their likelihood. We have developed the BSM algorithm to
uncover the shape parameters and transformation parameters of an arbitrary figure. The analyzed
regions correspond to the eyes, eyebrows and mouth region on which detection and tracking of
features is done using BSM. The results show that the estimation of the points is exact and
complies with the requests for this type of systems. Through quantitative analysis, the robustness
7. Computer Science & Information Technology (CS & IT) 531
of the BSM model in feature detection is evaluated, and it is maintained in nominal pose for a
range between [-400
to 400
] on X and [-200
to 200
] for Y . The used model and the methodology
of characterization showed efficiency to detect the emotion type in 95:55% of the evaluated
cases, showing a high performance in the sets analysis for the emotion recognition.In addition,
due to high accuracy in detecting and characterizing features proposed to estimate parameters
associated with the emotion type, the designed system has great potential for emotion recognition,
being of great interest in human/computer research systems.
Due to the current problem of facial pose changes in the scene, it would be of great interest to
design a 3D model that will be robust to these variations. Also, Taking advantage of sets analysis
demonstrated in this work, it would be interesting to develop a system that in addition to use the
sets analysis, add an adaptive methodology to estimate the status of each facial action unit to
improve performance of the emotion recognition system.
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