The International Arab Journal of Information Technology (IAJIT)


Analysis of Face Recognition under Varying Facial Expression: A Survey

 Automatic  face  recognition  is  one  of  the  most  empha sizing  dilemmas  in  diverse  of  potential  relevance  like  in  different  surveillance  systems,  security  systems,  a uthentication  or  verification  of  individual  like  criminals  etc.  Adjoining  of  dynamic expression in face causes a broad range of  discrepancies in recognition systems. Facial expression not only exposes  the  sensation  or  passion  of  any  person  but  can  also   be  used  to  judge  his/her  mental  views  and  psychoso matic  aspects.  This  paper  is  based  on  a  complete  survey  of  face  recogni tion  conducted  under  varying  facial  expressions.  In  order  to  analyze  different techniques,  motion-based,  model-based and   muscles-based  approaches  have been  used  in  order t o  handle  the  facial  expression  and  recognition  catastrophe.  The  analysi s  has  been  completed  by  evaluating  various  existing   algorithms  while  comparing  their  results  in  general.  It  also  expands   the  scope  for  other  researchers  for  answering  the  question  of  effectively  dealing with such problems.    
Approach Feature Extraction Classifier D ata Base Performance Important Points Dai et al. 2000

[15] Appearance based Calculate difference image from YIQ image Optical flow projection histogram for each expression is used to classify features -- Performance is calculated on the basis of classification of facial features 1. Compute optical flow histogram from adjacent frames. 2. Difference of image from YIQ image Zhang et al. 2001

[75] Appearance based Using AAMs -- -- Performance is calculated on the basis of classification of facial features 1. Used subspace method 2. Not successfully recognized identity of a person Analysis of Face Recognition under Varying Facial Expression: A Survey 381 Table 2. Assessment of model based facial expressio n. Model Based Approaches References Approach Feature Extraction Classifier Data Base Performance Important Points Gokturk et al. 2002

[28] Appearance based Stereo tracking algorithm Support vector machines (SVM) provides a robust classification -- Recognition rates up to 91% by classifying into 5 distinct facial motion and 98% for 3 distinct facial motion 1. Independent to view and posed variation Bourel et al. 2002

[6] Appearance based State based feature extractor Rank-weighted k- nearest neighbour classifier Cohn-Kanade facial expression database Recognition rate = 99% 1. Handle occlusion and noisy data 2. State based feature modeling Ramachandran et al . 2005

[57] Appearance based Control Points of the Candide model actually determines the transient features Implement PCA + LDA classifier Use FERET Database for neutral & smiling face The expression with expression normalized achieves 73.8% results 1. Model based Approach 2. Provides synthetic image using affine warping of the texture Fu et al . 2006

[24] Appearance based -- -- MPI Caucasian Face Database and AI&R Asian Face Database See result from reference #

[23] 1. Efficient for realistic face model. 2. Reduced computations via M- Face Bronstein et al. 2007

[7] Feature based -- -- Expression data base Minimum error = 7.09% 1. Embedding of geometric model with low dimension space leads to less metric distortions. 2. Representation of expression rather than generating expressions Bindu et al. 2007

[5] Appearance based 1. Discrete Hopfield Networks for feature extraction 2. Hough transform and 3. Histogram approach Reduced the size using PCA based classifier. Cohn-Kanade Action Unit Coded Facial Expression Database. Recognition accuracy of 85.7% 1. Model flexibly generates the number of emotions. 2. Cognitive emotions are sensed 3. Emotions are characterized with positive & negative reinforces. Martin et al. 2008

[46] Appearance based Using AAM based model AAM classifier set instead of MLP and SVM based classifier. FEEDTUM mimic database Anger emotion with average accuracy of 94.9% but other emotions are low between 10 to 30%. 1. Real time facial expression recognition. 2. AAM based model 3. Robust to lighting condition 4. False positive rate high for emotions except anger. Amberg et al. 2008

[1] Appearance based -- -- GavabDB database and the UND database 99.7% for GavabDB with improved speed 1. Handle noise 2. Recognition rate high Vretos et al. 2009

[72] Appearance based Model vertices are determined using PCA SVM based classifier Cohn-Kanade facial expressions database Classification accuracy achieved up to 90% 1. Good framework towards model based approach 2. Robust against 3D transformation operation on the face 3. Not sensitive to SVM configuration. Sun et al. 2010

[64] Appearance based 1. Locate ROI (region of interest) 2. Apply PCA to ROI to locate nose tip. PCA and LDA classifiers 4D face data base called BU-4DFE Expression dependent achieves up to 97.4% result. 1. Highlights the lack of control points 2. Focus on 4D data 3. Time consuming 4. Forehead area not specified 3.1.3. Muscles-Based Approaches Facial expressions engendered with the contraction of subcutaneous muscles that control and alter facial features like eye brows, nose, lips, eye lids and s kin texture etc. The muscles actions are distinguished in two facial parameters as. Facial Action Coding System (FACS) illustrates another way to measure the facial expression by examining the upper and lower FACS

[54]. FACS is a standard that offers uniform functionality as optic flow. FACS was initiated by Ekman and Friesen in 1978

[21 ] that defined 46 AUs in which 12 for upper face and 18 for lower face. The remaining are the grouping of different AUs constitute of additive AUs

[70]. Although there is a bit difference between FACS and facial muscles but it expresses the muscles contrac tion

[17]. Tian and Jan in 2001 presented an automatic f ace analysis method

[70] by tracking the transient and in- transient facial features and classified it as uppe r and lower AUs. Ashish Kapoor proposed a new idea of analyzing automatic AUs by tracking the pupil in th e eye

[39]. Similarly Pantic and Leon recognize facia l gestures in static and posed faces

[3, 54]. On the other hand in 2005 Zhang mingled dynamic Bayesian network (DBNs) with FACs

[53, 74]. Facial Animation Parameters (FAPs): It is the standard of SNHC, ISO/IEC developed by MPEG-4 coding system

[4, 62] emphasized on synthetic and animation that are allied with AUs. MPEG- standard used the static image with their associated 84 feat ure points FPs

[4]. Facial expressions on face illustra te visual impact on others which are usually controlle d by contraction of muscles, through subcutaneous muscles just under the skin. Entirely, human face restrains 43 muscles, also called mimetic muscles. One of the six basic facial signals like anger, dis gust, sadness, fear, surprise, happiness is an outcome of change of these muscles. In 2001 Choe and Ko

[11] introduced the concept of analyzing the muscles actuation in order to synthesize expressions. The field of muscles-based emotion recognition expanded further in 2004 when Ang et al.

[2] examined the facial muscles activities for computer s to automatically recognize facial emotions. Primari ly the emotions of male and female are captured from facial muscles through electromyogram Sensors (EMG) signals and were used to create feature 382 The International Arab Journal of Informa tion Technology, Vol. 10, No. 4, July 2013 templates. Ibrahim et al.

[34] expanded the work of Ang et al. in 2006

[2] and used surface electromyography (sEMG) to acquire facial muscles actions in different age categories with mean age o f 47.5 and 23 years old females. Similarly in 2008 a research was conducted by Takami et al. against quasi- muscles to quantify facial expressions by estimatin g FPs

[66]. On the other hand Jayatilake et al. in

[35] made an effort to restore facial expressions (smile recovery) by exploiting robot mask for paralyze patients. On the whole appraisal against muscles ba sed approaches are depicted in Table 3. 3.1.4. Hybrid Approaches Motion-Model Based Techniques: Hsieh et al.

[31] and

[32] proposed a relative algorithm of Optical Flow (OF) that provides the noticeable motion of objects, sur faces or edges in a visual scene. The main goal of this p aper is to adjoin the intra-person optical flow with neu tral images to synthesized faces. Finally the images are synthesized. Model Based Image Coding Techniques: During video transmission most of the information is attac hed with the first frame and is based on model as well as prior information of code from first frame that s w hy it is so called Model based Image coding , Knowledge based image coding or semantic image coding

[12] etc which controls the knowledge of Facial Expressi on Parameters (FEP). In contrast to conventional codin g systems Choi et al.

[12] improves the image quality and bit rate by transmitting the corresponding paramete rs instead of image itself. Eisert and Girod

[19] also present an analysis of 3 D motion by indicating geometry, texture, facial moti ons and facial expressions through model based coding systems. The exterior of the person are controlled with triangular B-splines model while Facial Animation Parameter (FAP) depicts facial expression. On the other hand Hiroshi Kobayashi and Hara in

[40] offers human machine interaction between 3D face robots with human beings. In order to examine the facial motion in the human face, 46 Aus are inspected with face robots. Essa and Pentland in

[22] basically present an analysis of basic coding action units that are usef ul to guess facial motions. The facial motions are estima ted by measuring optical flow. Finally construct the physics-based model by adding anatomical based muscles in Platt et al.

[56]. Similarly, Zhang et al. in

[76] show his effort in real time environment to produce synthetic image. FACs is incorporated with deformed physical based spring model to approximate animated facial muscles using lagrangian dynamics. According to Kuilenburg et al. in

[14, 42], automatic facial expression recognition is not an effortless chore since pose, illumination and expression variation is the grand dilemma in the pertinent field. Model-Muscles Based Techniques, Ohta et al. in

[51] used exhaustive anatomical knowledge by exploiting muscle based feature models to track fac ial features such as eye brows, eyes and mouth. On the whole, the main emphasis to this approach is to provide the deformable models. Tang et al. in 2003 attempts to create a control B-splines curves (NURB S) by generating a motion vector in order to control f acial expressions

[68]. The general description of hybrid based approaches is shown in Table 4. Table 3. Assessment of muscles based facial express ions. Muscles Based Approaches References Approach Feature Extraction Classifier D ata base Performance Important Points Choe et al 2001

[11] Feature based Tracking of muscles contraction via optical capture system -- Algorithm is implemented on PC platform Artist-in-the-loop method provide superior results 1. Analyze muscles actuation 2. Easy to control facial expressions 3. Provide synthetic images Ang et al 2004

[2] Feature based Features extract using EMG electrodes Minimum distance classifier -- Achieves 85 to 94.44% accuracy 1. Emotion analysis on male and female 2. Use EMG signals to create feature templates Ibrahim et al 2006

[34] Feature based Use sEMG instead of EMG signal -- -- Spectral density range is between 19 to 45 Hz 1. Utilize surface EMG 2. Applied on different age categories Takami et al 2008

[66] Feature based Displacement of controlled feature points -- -- Quasi-muscles is helpful for tracking FPs 1. Easy to estimate FPs using quasi- muscles Jayatilake et al 2008

[35] Feature based Features extract using EMG -- -- Greater displacement at grid points 2 and 6

[35] Artificial smile recovery method via robots 2. EMG based facial expression analysis 4. Classification The final phase of automatic facial expression recognition classifies the transient and in-transie nt facial features in accordance with the desired resu lt. Selecting a low dimensional feature subspace from thousands of features is a key phenomenon for optim al classification. The main ambition to use subspace classifiers is to convert high dimensional input da ta into low dimensional feature subspace. Subspace classifiers selectively represent the features that minimize the processing area. Feature extraction pl ays a vital role to reduce the computational cost and Analysis of Face Recognition under Varying Facial Expression: A Survey 383 progress the classification results because selecti ng a low dimensional feature subspace from bundle of features is very crucial for optimal classification . Wrong features selection degrades the performance o f face recognition; even though superlative classifie r may be used. There are bunch of linear and non-linear classifier s that offers categorization between cor related and uncorrelated variables. The two basic linear classification techniques are principal component analysis PCA

[10, 16, 31, 32, 42, 59], and Linear discriminant analysis LDA

[10, 31, 32, 58], Others classifiers are Independent Component analysis ICA, Support vector machine SVM

[10, 18, 75], Singular Value decomposition SVD and kernel versions classifiers like KPCA, KLDA, Rank weighted k-neares t neighbors k-NN

[32], elastic bunch graph algorithm, AAM

[65], Active Shape model ASM, Minimum distance classifier

[2], Back propagation neural ne twork

[36, 40, 45] and 3D morph able model based approaches are commonly used. For supplementary aspect Tsai and Jan analyze different subspace mode l in

[71]. 5. Database The good choice of database under uncontrollable condition like occlusion and pose, illumination, expression variation is a very challenging task tha t deals with testing the novel approaches. Databases are used to test the proposed system on different image s under varying condition like pose, illumination, occlusion, expression etc. Some databases are publically available for researchers. In some cases various databases stores the preprocessed data of images for learners. One subject or individual has number of samples in different varying conditions. Number of databases includes FERET, CMU-PIE, Extended YaleB, Cohn Kanade, AR, ORL, Japanese Female Facial Expression JAFEE, Indian Face databas e etc. In all, FERET face database and CMU (PIE) pose , illumination and expression face database is the on e which are de-facto standard and are very courageous to handle different problem domain. In contrast to FER ET database there are some common expression databases which is openly available that are Cohn-Kanade database sometimes stated as CMU-Pittsburg AU coded database which has posed expressions

[38] and is no t fit for spontaneous expressions. Similar posed expressi on database are AR face database

[47], Japnese Female Facial Expression Database (JAFFE)

[33] etc. 6. Discussion and Comparasion The goal of each technique mentioned above is to recognize faces under varying facial expressions. E ven though some approaches provide desired results but do not offer more accurate domino effect. In order to evaluate the vulnerability of such approaches, the comparison chart has been drawn as in Table 1 (Motion-based), Table 2 (Model-based), Table 3 (Muscles-based) and Table 4 (Hybrid Approaches). Motion based approaches are extensively used to estimate the degree of face deformation and intensi ty variation

[75], that endow comprehensive informatio n about local and global features but it takes much t ime to estimate pixel by pixel motion vectors. These motion vectors are provided by the detailed information

[22]. On the other hand in model based advancements CANDIDE model is used as a reference image that improves the accuracy of such systems

[5 6, 72]. This reference image is helpful for recognizin g facial expressions

[1, 7] and can be used to produc e animation

[46, 49, 76] and synthetic images

[46, 57 ] but the main constraint across this approach is the boosted complexity

[1] while estimation of mesh for constructing model is not an easy task

[22]. Model based techniques are also reliable for real time sy stem because of corresponding triangle to triangle mappi ng rather than pixel by pixel transformation

[46, 76]. Though it present more detailed information across edges but not trustworthy for texture transformatio n due to lower anatomical information

[42] so, much o f the researchers overcome this issue using muscles based algorithms. Similarly, muscles based approaches are powerful that are provided by detail ed anatomical information

[52] while facial features a re tracked by only locating the varied features and th e direction of muscles shifting

[51] but it also incr eased the complexity

[11]. Facial muscles anatomical aspe ct are also supportive to judge the patients muscles activities which are unable to produce expressions on faces

[35] but various diseases and facial warping become powerless to extract facial features

[66]. Facial muscles can be monitored through coding system which is an image based technique

[17]. In order to diminishes the complexity of muscles based approaches coding systems like FACs, FAPs, Emotional Facial Action Coding System (EMFACs), Facial Action Scoring Technique (FAST), Maximally Discriminative Facial Movement Coding System (MAX), Facial Electromyography (EMG) etc., are the reliable measure that increase the accuracy rate wh ile speed up the system

[17]. Here is an interesting th ing that more classification are provided by the facial actions the more it provides the detailed informati on

[17] but less classification causes lack of tempora l and spatial knowledge

[74]. Exactness of the images increased across the assigned code area but is not good for texture transformation because Action units are basically local spatial

[17, 74]. Another constrain t of this system is that it becomes more complex for automatic machine facial expression recognition

[17 ]. In combination to such approaches like motion-model based technique that estimate the intensity variati on for feature extraction and use CANDIDE model for face recognition

[31]. Likewise model based im age 384 The International Arab Journal of Informa tion Technology, Vol. 10, No. 4, July 2013 Table 4. Judgment of hybrid approaches. Motion-Model Based Approaches References Approach Feature Extraction Classifier Data base Performance Important Points Hsieh et al 2009

[31] & 2010

[32] Feature Based Calculate intra- person OF from inter- person + overall OF PCA + LDA based classifier Binghamton University 3D Face Expression (BU-3DFE) database Average recognition rate of 94.44% 1.Time taken by OF-Syn and OF is 2.01 and 1.43s respectively 2. Costly Model Based Image Coding Choi et al 1994

[12] Appearance based Encoding & Decoding with muscle based Aus of de-facto standards Deforming rules for 34 AU for both upper & lower faces -- Texture update: Method I: 1.Less bit rate 2. Low quality image Method II: 1. Improves quality image 2.Large memory space No texture update: Estimated bit rate 1.4, 3.5 & 10.5 Kbits/s 1. Facial Expression video transmission 2. Image synthesize (decoding) 3. Texture update improves the quality of image 4. Handle head motion Eisert et al 1997

[19] Feature based Encoding and Decoding with FAPs of ISO/IEC standard developed by MPEG. FAPs of ISO/IEC standard developed by MPEG. -- Estimated bit rate of less than 1kbit/s with error rate of 0.06% in each frame for both synthetic & video sequence 1.Estimate 3D motion with facial expression 2. B-splines are suitable for modeling facial skins Kobayashi et al 1997

[40] Feature based Data acquired using CCD camera Back propagation Neural Network ensemble classifier -- Achieve recognition rate of 85% 1. Human machine interaction between robots & human beings Essa et al 1997

[22] Feature based Optical flow based approach FAC+ instead of FAC Database of 52 sequence Recognition accuracy 98% 1. Efficient in terms of time & space Zhang et al 2001

[76] Appearance based FACs FACs based anatomical spring model Facial Modeling using Open GL/C++ -- 1. Based on physical anatomical information 2. Real-time based synthetic image 3. Analyze relationship btw deformed facial skin and inside state Kuilenburg et al 2005

[42] Appearance based Delauny triangulation 1. PCA based classifier that converts the shape into low dimensional space. 2. FACs -- Emotional Expression classifier accuracy up to 89% while Aus detect with average accuracy of 86%. 1. Use holistic based approach 2. Back propagation trained neural network. 3. Use trained classification network. Model-Muscles Based Approaches Ohta et al 2000

[51] Feature based Muscle based control points -- -- Facial parameters like eyebrows, mouth corners and upper lip shows effective results. 1. Muscle based feature modeling 2. Provide deformable models Tang et al 2003

[68] Appearance based Reference and current NURBS control points -- VC++/Open GL The more the NURBS flexible the more it gave the desired results 1. Control facial expressions via NURBS 2. FACS based implementation Chin et al 2009

[10] Appearance based Rubber band method -- Not based on 3D data base Surprise achieve 8.3, fear = 5.5, disgust = 7.2, anger = 8.7, happiness = 8.0 and sadness = 8.9 1. Transform facial expression in a target face 2. 3D face model coding is a technique, preferable for texture transformation and for corresponding edge matching for face recognition

[12, 40]. Since, model muscles based techniques takes the advantage of couple of model and muscles based technique respectively for face recognition under varying facial expressions. Though missing facts (texture) are provided by anatomical muscles based algorithms whereas complexities are reduced using CANDIDE model as a reference image

[51, 68]. 7. Conclusions Facial expression are fabricated during communicati on transmission so images may be acquired in uncontrollable condition like occlusion (glasses, s carf, facial hair, cosmetics and it also effects recognit ion rate), pose, illumination and expression variation etc. Facial Expression not only exposes the sensation or passion of any person but also used to judge his/he r mental views and psychosomatic aspects. Classification of different facial expression recognition algorithms provides a way to analyze th e emotions produced by human faces. It helps to answe r the question of which techniques are practicable in which type of environments. Various researchers hav e taken advantage by utilizing the rapid assigned cod e from the dictionary of diverse of coding system techniques i.e., FACs, FAPs etc during face recognition process. Similarly, model is used to sp eed Analysis of Face Recognition under Varying Facial Expression: A Survey 385 up the recognition method. This paper provides a snapshot of different algorithms which are very hel pful for other researchers to enhance the existing techn iques in order to get better and accurate results. References

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[76] Zhang Y., Prakash E., and Sung E., Real-Time Physically-Based Facial Expression Animation Using Mass-Spring System, in Proceedings of Computer Graphics International, Hong Kong, pp. 347-350, 2001. Marryam Murtaza recived her BSc degree from COMSATS, Pakistan, in 2008. She is a student of MSc in Computer Science at COMSATS Wah. Currently, she is working on her thesis. Her research interests include digital image processing and software engineering. Muhammad Sharif has been an assistant professor at Department of Computer Science, COMSATS Institute of Information Technology Pakistan. He is also PhD Scholar at COMSATS Institute of Information Technology. He has more than 17 years of experience including teaching graduate and undergraduate classes. Mudassar Raza is a Lecturer at COMSATS Institute of Information Technology, Pakistan. He has more than four years experience in teaching undergraduate classes at CIIT Wah, Mudassar Raza. Also, he has been supervising final year projects to undergraduate students. His areas of interest include digital image processing, and para llel & distributed computing. Jamal Hussain Shah is an associate researcher in Computer Science Department at COMSATS Institute of Information Technology, Pakistan. His research areas include digital image processing and networking. He has more than 3 years experience in IT-related projects , he developed and designed ERP systems for different organizations of Pakistan.