Robust Landmark Localization and Tracking for Improved Facial Expression Analysis

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1 Robust Landmark Localization and Tracking for Improved Facial Expression Analysis Hamdi Dibeklioğlu, Albert Ali Salah, Theo Gevers Intelligent Systems Lab Amsterdam, University of Amsterdam {h.dibeklioglu, a.a.salah, Keywords: Facial landmarking, facial expression analysis, structural prior Abstract Automatic facial expression analysis requires localization and tracking of facial features in an accurate and robust manner. We describe a statistical method for automatic facial landmark localization, which is then used to improve a state-ofthe-art deformable tracking based facial expression recognition algorithm. Our landmarking is based on Gabor wavelet features modeled with mixtures of factor analyzers. Once the landmarks are automatically located, they are used to initialize a tracker that maintains a simplified 3D face model matched to the appearance of the face. The deformations are classified with a naïve Bayes scheme into six expression categories. We test the proposed methods extensively on cross-database experiments conducted on FRGC, Cohn-Kanade, and Bosphorus face datasets. Our results show that the statistical landmarking method we propose is robust in its generalization and increases the accuracy of expression recognition significantly. 1 Introduction Evaluation of facial expression relies on accurate face detection, face registration, localization of fiducial points in faces, and classification of shape/appearance information into expressions. When temporal information is available, tracking and temporal modeling also enter the picture. The pipeline of a facial expression analysis method starts with face detection, and often proceeds by locating several fiducial points on detected faces, also called anchor points, or landmarks. Usually, corners of eyes and eyebrows, centers of irises, nose tip, mouth corners, and the tip of the chin are used as key landmarks. These key landmarks are generally sufficient for face registration. However, more landmarks ( to points) are required in facial expression analysis. Facial surface deformations caused by expressions can be described by movements of selected facial feature points. If these points are discriminative enough, and are detected accurately, deformation analysis can classify facial expressions. Nevertheless changing pose, resolution and illumination conditions make facial landmark localization a challenging problem. Especially, statistical models can fail if the variation shown in the training set is not sufficient enough for generalization of unseen test samples. In this paper we describe a system for facial expression analysis which encompasses six basic emotional expressions (i.e. happiness, fear, surprise, anger, disgust, and sadness). Our emphasis is however on automatic landmarking, the effect of which we assess on subsequent expression categorization. We improve a recent facial landmarking algorithm by introducing a prior conditioned on face detection. We show via extensive cross-database tests that the landmarking method we use is robust. Our subsequent tests on emotion recognition establish that accurate statistical descriptions of several landmarks can be successfully used to improve expression analysis through tracking; we obtain as much as per cent classification accuracy improvement through the use of statistical landmarking. This paper is structured as follows. In Section 2, an overview of the system is presented. Section 3 describes related work in landmarking, followed by Section 4 that describes our statistical landmark localization algorithm. In Section 5, we present the algorithms for model-based facial tracking and expression analysis. The experimental results are presented in Section 6, followed by our conclusions in Section 7. 2 Overview of the System In this section, we briefly describe the overall pipeline of the system (See Figure 1). The camera 1

2 (or video) input is analyzed first for the presence of a face. We use the Viola-Jones algorithm for face detection [23]. While the system supports continuous expression evaluation, the analysis is differential and the best results are obtained by presenting a neutral, frontal face image during the initialization of the tracker. Data acquisition Face detection Histogram equilization Gabor wavelets Facial feature localization Tracker initialization Facial feature tracking Expression classification Viola-Jones algorithm IMoFA model for features GOLLUM structural analysis algorithm TPS algorithm Active appearance model PBDV tracker Motion unit extraction Naïve Bayes classifier Figure 1: Overview of the system. Once the face is located, the landmarking module is used to locate seven facial features. These are the inner and outer eye corners, the nose tip and the mouth corners. The statistical feature modeling approach via incremental mixtures of factor analyzers (IMoFA) algorithm [17], as well as the GOLLUM structural analysis algorithm to correct landmarking errors [18], will be detailed in Section 4. The located landmarks are used to initiate a piecewise Beziér volume deformation tracker [21]. An initial generic face mesh is warped to the shape indicated by the detected landmarks with the thin-plate spline algorithm [3]. The tracking module also has a general appearance model associated with it to prevent the accumulation of tracking errors [6]. This model is enhanced with a virtual structuring element to reduce the effect of different backgrounds in training and testing conditions [22]. The expression classification is performed on a set of motion units, which indicate the direction of movement of several key points on the Beziér volume with respect to the initial, neutral frame. We use a naïve Bayes classifier to compute the posterior probabilities for the six basic expression categories. 3 Related Work in Landmarking Facial landmarking is the first and indispensable step of many human-related computer vision applications especially for facial expression analysis. Expression analysis requires costly manual initialization in the absence of a reliable landmarking algorithm [5]. Automatic localization of facial feature points is a challenging problem because of varying acquisition conditions (i.e. illumination and resolution) and occlusion problems. Besides, spatial relations of facial landmarks show variations for different face images. Facial deformations based on expressions, and acquisition noise also affect patterns of landmarks. Facial landmarking techniques can be divided into two main categories as texture-based and structural methods. Texture-based methods use features based on intensity information and texture values around the related landmarks. Localization of the eye and mouth regions by vertical projection histograms of intensity values [4, 28], and eye detection by using the contrast differences in the eye region [1, 11] can be counted as examples for texture-based landmarking. Structural methods incorporate shape information by looking at the relative positions of landmarks, combining the shape features with local appearance features. Active appearance models are typical examples for structural landmarking, and many variants on this method are introduced [26]. Additionally, there are several studies that use both structural relationships between landmark locations, and local feature constraints [, 25]. In [25], Gabor feature jets are used to model local features, and the best match for each feature point is found by an exhaustive search on template library (called the bunch). In [7], Cristinacce and Cootes used an AdaBoost classifier based on Haar wavelet features [23] for coarse-level localization and an Active Shape Model (ASM) was employed for fine-tuning. ASM approaches jointly estimate all landmarks, combining local feature information with shape. For cases where local features are missing or misleading (e.g. natural occlusions, scars, accessories) joint estimation methods will experience problems. Furthermore, ASM and similar methods require greater number of landmarks for training; usually - landmarks are used per face. However, none of the major databases has this kind of ground truth. Consequently, [24] and [18] focus on independent detection of landmark points. In [24], Vukadinovic and Pantic have proposed a method that uses boosted classifiers based on gray scale texture information and Gabor wavelet features. After the detected face region is divided into regions of interest (ROI), GentleBoost templates are used to detect landmarks within the relevant ROI, independently. However, this method is not very effective for expressive faces, as it uses 2

3 heuristic location assumptions for ROI specification. Salah et al. proposed a coarse-to-fine strategy for independent feature point detection in [18]. On the coarse scale, Gabor wavelet features are statistically modeled, and complemented with a structural analysis step. Then, discrete cosine transform (DCT) features are used for fine-tuning of the coarsely localized landmarks. In this approach, the whole image is searched for each landmark. However, the search space can be drastically constrained by face detection through a shape prior. Unlike using discrete regions as defined in ROI-based methods, this kind of shape prior would estimate continuous probability values for the search space. We introduce such a prior in this paper and show that through such a prior, contrasted landmarking methods become faster and more accurate. 4 The Facial Landmarking Algorithm 4.1 Facial Model and Features In the onset of our approach, we detect the face with the Viola-Jones face detection algorithm [23]. We therefore assume that the face image is roughly frontal. Occasional detection failures do not affect the method because system works on camera input (or video), and switches to tracking once the face is detected. To reduce the computational complexity, we downsample the cropped high resolution face images into pixels and apply a histogram equalization to the cropped area for damping the illumination effects. Let (x,y) denote the relative coordinates of a particular landmark within the cropped face area. A prior distribution p(x,y l i ) is learned for each landmark i during the training stage. Here, i = 1...7, and l i denotes the selected landmark. This shape prior is well approximated with a 2-dimensional Gaussian distribution. We constrain the search for landmarks to areas delimited by the shape of this prior distribution. When compared to the approach presented in [18], this constraint provides an immediate speed-up in feature extraction (up to two orders of magnitude), as well as making use of the structural information between individual landmarks. Independent Gabor wavelet feature patches are extracted from around a 7 7 neighborhood of each landmark candidate. The resulting feature vectors (49-dimensional) are calculated as follows, for each orientation w and scale v: Ψ j ( x) = k j k T j σ e ( 2 kj = kjx, ) k jy ( k j k T j x x T 2σ 2 ) [ e (i k j x T) e ( σ 2 = (k v cosφ w,k v sinφ w ) 2 )] k v = 2 v+2 2 π,φ w = w π 8, (1) where x = (x, y) is a candidate landmark location and j is an index for different kernels. k j and x are both expressed as row vectors. The standard deviation of the Gaussian function is selected as σ = 2π. The first factor in the Gabor kernel represents the Gaussian envelope and the second factor represents the complex sinusoidal (carrier) function. The term, e σ2 /2 in the square brackets compensates for the DC value. These features are extracted in 8 orientations, w {0,1,2,3,4,5,6,7}, and at 5 different scales, v {0,1,2,3,4}. The training uses only the positive samples of each landmark class, obtained from ground truth. The features are min-max normalized before statistical modeling. 4.2 Statistical Feature Modeling We follow [18] in our model choice for statistical distribution of extracted features and use the Incremental Mixtures of Factor Analyzers (IMoFA) algorithm. This approach involves placing a number of Gaussian distributions on the data incrementally and in unsupervised fashion [17]. Through a latent variable assumption, the high-dimensional full covariance matrix of each component in the mixture distribution is expressed in a lower dimensional manifold, resulting in a locally adaptive model complexity. Such model simplification in terms of parameters improves generalization, as the number of training samples is fixed. The trade-off between accuracy and complexity is explored in a greedy and unsupervised fashion, and difficult portions of the data are modeled with more parameters automatically. The latent variable assumption reflects on the expression of the data covariance matrix as follows: Σ j = Λ j Λ T j +Ψ, (2) where j stands for the components in the mixture model,σis the covariance matrix (d d),λis the factor loading matrix (d p) that transforms the data from the p-dimensional manifold to the d-dimensional data space, Ψ is a diagonal matrix with d free parameters that stands for independent variance in each dimension. Usually p << d, which makes the model a parsimonious representation. Note that the model complexity is somewhere between a diagonal-covariance and a full-covariance mixture. Given a dataset, the IMoFA algorithm initially fits a Gaussian distribution generated from a single dimensional manifold to it, and then proceeds by increasing the complexity. The two possible operations at any iteration are splitting an existing component, or adding a factor to an existing component. A validation set is monitored for stopping the iterations. At each iteration, the Expectation-Maximization (EM) 3

4 algorithm is used to converge to a locally optimal maximum likelihood solution [9]. If we compare this approach to the popular unsupervised Gaussian mixture model learning procedure (ULF) proposed by Figueiredo and Jain [8], we can note down a few things. The ULF method proposes to fit a large number of Gaussians to data, and proceeds by merging them until a single component remains. Then all intermediate models are evaluated with a Bayesian complexity criterion, and one model is selected. The initial fitting of many components is impractical and costly for problems with high dimensionality. Also, the resulting models in ULF have either full or diagonal covariances. IMoFA explores a range of models in between these extremes, and this property is also very important for high-dimensional problems, since the gap between diagonal and full covariance models widens quadratically with the number of feature dimensions. We extract Gabor features from the neighborhood of each landmark l {1,2,3,4,5,6,7} (four eye corners, two mouth corners and the nose tip). If we denote the Gabor feature vector with u, the mixture model can be written as: p w,v (u) = j p w,v (u G j )p w,v (G j ), (3) wherew andv denote the orientation and scale of the Gabor filter, respectively. G j describes a Gaussian distributionn (µ j,σ j ), andj is an index for the components in the mixture. p w,v (G j ) is the prior probability of the component i along orientation w at scale v, and p w,v (u G j ) is the probability that u is generated by component i. Our approach in this paper is similar to [18], which also uses IMoFA models for Gabor wavelet features. However, a single scale of the Gabor wavelet was used in [18], and no shape prior was considered for conditioning the landmarking on the facial boundary. We have dispensed with the coarse-to-fine processing proposed in [18] in favor of a faster, one-shot detection, but retained the fast structural analysis procedure, which is observed to improve landmarking even with the shape prior we propose. 4.3 Structural Analysis The GOLLUM algorithm is proposed in [18] to test the consistency of a shape, expressed by a set of landmarks, by contrasting it with a set of learned models. The affine normalization of a shape, which is a regular step in shape comparison, can be erroneous if one of the landmarks is off its mark. To remove such effects from normalization, GOLLUM checks all possible normalizations based on landmark triplets, and chooses the one that has the strongest empirical support. Then, the landmarks in the chosen set (a) (b) Figure 2: (a) The Bézier volume model. (b) The motion units. can be used to detect and correct the erroneous landmarks automatically. We refer the reader to [18] for the details of the GOLLUM algorithm. On the FRGC dataset [18] reports 5 to per cent localization improvement with the use of GOLLUM although the ceiling effect prevents a complete assessment. On artificially disturbed data, GOLLUM can correct one or two corrupted landmarks out of seven with more than 98 per cent accuracy. For this reason, it is most useful against total feature occlusions. In Section 6, we report our accuracy results with the GOLLUM algorithm, and report its contribution on the average. 5 Application: Expression Classification Automatically located landmarks are used for initialization in an expression classification application. In recent years, facial expression analysis has been an active topic for researchers. Most studies focus on detecting facial actions units (AUs), and relating expressions to these AUs [2]. Despite a remarkable progress in AU detection, these systems require costly AU annotations for training, prepared by experts. We describe here a simplified approach for classifying six basic emotional expressions. This kind of systems can be trained by using video segments which are annotated for the main expression categories. For more elaborate expression analysis methods and recent developments, see [15] and [27]. Our method for expression analysis is based on [22], which employs a face model described by 16 surface patches embedded in Bézier volumes, shown in Figure 2 (a). To trace the motion of the facial features, a Piecewise Bézier Volume Deformation (PBVD) tracker is used [21]. This tracker is initialized by transforming the face model with respect to the detected face area. The alignment is originally performed by matching the facial boundary to the model. Our contribution to the system is a better initial alignment through the detected landmarks. We use the thin-plate spline (TPS) deformation algorithm to warp the generic face model to the auto- 4

5 matically detected shapes [3]. This approach transforms landmarks exactly to their target points, and interpolates all the other points. During the development of our algorithm, we have also tested Procrustes alignment [] as an alternative, but TPS was more accurate in our experiments. In general, we expect this to be the case when the landmarks are sufficiently accurate. A simple but efficient naïve Bayes classifier is used to classify facial expressions. As an advantage, the posterior probabilities allow a soft output of the system, which is usable as a continuous input to any facial affect-based system. Quantized movement vectors, extracted from a number of locations on the model, are passed as input to the classifier. These vectors are called motion units. Although the motion units are similar to AUs, they are simpler and tailored towards basic expression categories (See Figure 2 (b)). 6 Experimental Results 6.1 Experimental Setup and Data The Bosphorus, FRGC and Cohn-Kanade databases are used to train three versions of our automatic landmarking system. We do this to measure sensitivity to training conditions. Ground truth for seven landmarks on these databases are annotated manually. For testing the facial expression analysis system, the Cohn-Kanade dataset is used. The Bosphorus and FRGC sets are composed of static images, whereas the Cohn-Kanade dataset has video sequences, and is thus adequate for dynamic measurement of expressions. For each database, we use separate training, validation and test partitions, which have completely different subjects from each other. Also, there are no overlapping subjects between databases. Details of the Bosphorus, FRGC and Cohn-Kanade databases are given below. The Bosphorus database [19] for facial expression analysis has systematic expression and pose variations. There are 61 male and 44 female subjects (29 of which are professional actors and actresses) with a total number of 52 face images. The texture images are of high quality, and acquired under controlled studio light. Only frontal samples of the Bosphorus database have been used in our landmark localization experiments. For fair comparison with the results obtained on the Bosphorus dataset, we use the Spring 04 subset of FRGC database [16], which is the most challenging setting. It contains 2114 face images (neutral and uncontrolled expressions) from 465 subjects, in 6 4 resolution acquired under uncontrolled illumination conditions. The Cohn-Kanade AU-Coded Facial Expression Database [12] consists of approximately 0 image Figure 3: Samples from Cohn-Kanade, Bosphorus and FRGC databases, respectively. Table 1: Abbreviations and the size of training, validation, and test sets for each database. Database Abbr. Train Validation Test Bosphorus BOS Cohn-Kanade (Neutral) CK Cohn-Kanade (Expr.) CK-Ext FRGC FRGC sequences from subjects, each starting with a neutral face and showing a basic expression. Only frontal images are open to public use, and we only use those (249 sequences). Image resolution is6 4 pixels. Furthermore, two different datasets are prepared by taking the first (neutral) and the most extreme expression frames of these sequences, respectively. Since there are few sequences in Cohn- Kanade database, we use three-fold cross validation in our expression classifications experiments. All the databases we have used are freely available to the research community. Figure 3 illustrates the scale, pose, expression, resolution and illumination conditions across these three databases. The sizes of training, validation and test sets for each database are given in Table Automatic Landmark Localization Proposed landmarking algorithm has been tested on seven landmarks as four groups: outer eye corner(s), inner eye corner(s), nose tip, and mouth corner(s). Challenging cross-database conditions have been used to measure the accuracy of the landmarking algorithm. The state-of-the-art results in the literature vary 5

6 according to the acceptance threshold they used and the database employed for reporting. The acceptance threshold is the distance from the ground truth location beyond which a given landmark is deemed incorrect. Typically, this threshold is given in terms of inter-ocular distance. For example, in [7], Cristinacce and Cootes use per cent of inter-ocular distance as acceptance threshold, and report 95 and 92 per cent average localization accuracy for finding 22 landmarks on BIOID and XM2VTS databases, respectively. In [24], Vukadinovic and Pantic report 93 per cent average localization accuracy for Cohn- Kanade database by accepting points within per cent of inter-ocular distance to their ground truth locations. Kozakaya et al. report 95.1 per cent average localization accuracy for their weighted vector concentration approach [13] and 94.2 per cent for the modified active shape model presented in [14] on the FERET database, with per cent inter-ocular distance as threshold. In this paper, we report our results with per cent of inter-ocular distance as acceptance threshold. Table 2 shows the correct localization accuracies for different training and test sets. The reported success rates are obtained by accepting points within per cent of inter-ocular distance to the ground truth. Correct localization accuracies for different acceptance conditions are also given in Figure 4. Intersubject variation of the ground truth is about 5-7 per cent of inter-ocular distance. When the proposed system is trained and tested on the same database, the average accuracy is 93.5 per cent for Bosphorus, and 92.7 per cent for FRGC databases. These are comparable to reported state-of-the-art figures. The published implementation of the system proposed in [24] allows us to compare it with our method. Since this system has been trained on Cohn-Kanade database, we have joined neutral and extreme expressions of Cohn-Kanade (denoted as CK-Both) and trained our system for comparison. Our system has 84.5 and 79.6 per cent cross-database accuracy for FRGC and Bosphorus databases, respectively. However, the system proposed in [24] can reach only 79.8 and 77.8 per cent accuracy, respectively for the same datasets. Our experiments show that mouth corners are detected with less accuracy than the other landmarks. This is an expected result as the mouth corners are affected more under expression changes. The variation on training set naturally affects the accuracy of the system. Uncontrolled acquisition of FRGC database provides more pose variations than the frontal subset of Bosphorus. Consequently, the shape prior implicates a larger area and this results in higher accuracy with FRGC training. Although we do not show the net effect of GOLLUM, it increases the accuracy of the proposed method by one per cent on the average. Table 2: Cross-database accuracy of the landmarking algorithm. Database Landmark Type Training Test O. Eye I. Eye Nose Mouth C. BOS BOS FRGC FRGC FRGC BOS FRGC CK FRGC CK-Ext BOS CK BOS CK-Ext BOS FRGC CK-Both BOS CK-Both FRGC Since the time complexity of GOLLUM is negligible, we retain GOLLUM in our system. 6.3 Facial Expression Classification The effect of landmark localization on expression classification is given in Table 3. The results are reported without landmarking (baseline), with landmarking trained on FRGC or Bosphorus databases, and with landmark ground truth for Cohn-Kanade database. For different expressions, the accuracy improvement is different; happiness, fear and disgust greatly benefit from improved alignment. Sadness sadly does not, because it mostly relies on eyebrow movements. Eyebrows do not have consistent statistical properties, and are very difficult to detect with statistical methods. The baseline accuracy of.68 per cent is increased to 75. per cent via FRGC training. 7 Conclusions A statistical landmark localization method has been described with robust cross-database accuracy, and a state-of-the-art facial expression classification algorithm has been improved by the proposed landmarking method. The ground truth for the indicated landmarks would apparently have provided a doubled improvement, which means that there is room for improvement in landmarking, as well as in classification. While the shape prior we have introduced greatly constrains the search for landmarks, cascade-based hierarchical search is still an attractive future direction. The face model we use needs to be enhanced to take into account much greater pose variations. It is a challenge to do this without sacrificing the speed of the expression analysis algorithm, which is around 15 fps in the present setup. As a related future work, the landmarking module will be tested for initializing the tracker on arbitrary poses. 6

7 Tested on BOS Inner Eye C. Outer Eye C. Nose Tip Mouth Corners Accuracy (%) Trained on BOS Trained on FRGC Trained on CK Both Tested on FRGC Inner Eye C. Outer Eye C. Nose Tip Mouth Corners Accuracy (%) Trained on BOS Trained on FRGC Trained on CK Both Tested on CK Inner Eye C. Outer Eye C. Nose Tip Mouth Corners Accuracy (%) Trained on BOS Trained on FRGC Tested on CK-Ext Inner Eye C. Outer Eye C. Nose Tip Mouth Corners Accuracy (%) Trained on BOS Trained on FRGC Figure 4: Landmark localization accuracies on Bosphorus, FRGC and Cohn-Kanade databases with different training conditions. Acceptance threshold indicates the acceptance distance to the ground truth as a rate of interocular distance. 7

8 Table 3: Emotion recognition accuracies for the extreme frames on the Cohn-Kanade database. Lm.Support Happy Surprised Angry Disgusted Frightened Sad Avg.(Emotions) Avg.(Samples) None Auto(BOS) Auto(FRGC) Ground Truth References [1] S. Arca, P. Campadelli, and R. Lanzarotti. A face recognition system based on automatically determined facial fiducial points. Pattern recognition, 39(3): , 06. [2] M. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, and J. Movellan. Recognizing facial expression: machine learning and application to spontaneous behavior. In CVPR, volume 2, page 568, 05. [3] F. Bookstein. Principal warps: thin-plate splines and the decomposition of deformations. TPAMI, 11: , [4] L. Chen, L. Zhang, H. Zhang, and M. Abdel-Mottaleb. 3D shape constraint for facial feature localization using probabilistic-like output. In AFGR, 04. [5] J. Cohn, A. Zlochower, J. Lien, and T. Kanade. Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding. Psychophysiology, 36(01):35 43, [6] T. Cootes, G. Edwards, and C. Taylor. Active appearance models. TPAMI, 23(6): , 01. [7] D. Cristinacce and T. Cootes. Automatic feature localisation with constrained local models. Pattern Recognition, 41:54 67, 08. [8] M. Figueiredo and A. Jain. Unsupervised Learning of Finite Mixture Models. TPAMI, pages , 02. [9] Z. Ghahramani and G. Hinton. The EM algorithm for mixtures of factor analyzers. Technical Report CRG- TR-96-1, University of Toronto, (revised), [] C. Goodall. Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society B, 53(2): , [11] S. Ioannou, M. Wallace, K. Karpouzis, A. Raouzaiou, and S. Kollias. Combination of multiple extraction algorithms in the detection of facial features. In ICIP, volume 2, pages , 05. [12] T. Kanade, J. Cohn, and Y. Tian. Comprehensive database for facial expression analysis. In AFGR, 00. [13] T. Kozakaya, T. Shibata, M. Yuasa, and O. Yamaguchi. Facial feature localization using weighted vector concentration approach. Image and Vision Computing, 09. [14] S. Milborrow and F. Nicolls. Locating facial features with an extended active shape model. In ECCV, pages 4 513, 08. [15] M. Pantic and L. Rothkrantz. Automatic analysis of facial expressions: The state of the art. TPAMI, 22(12): , 00. [16] P. Phillips, P. Flynn, W. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. Overview of the face recognition grand challenge. In CVPR, volume 1, pages , 05. [17] A. A. Salah and E. Alpaydın. Incremental Mixtures of Factor Analysers. In ICPR, volume 1, pages , 04. [18] A. A. Salah, H. Cinar, L. Akarun, and B. Sankur. Robust Facial Landmarking For Registration. Annals of Telecommunications, 62(1-2): , 07. [19] A. Savran, N. Alyüz, H. Dibeklioğlu, O. Çeliktutan, B. Gökberk, B. Sankur, and L. Akarun. Bosphorus database for 3D face analysis. In BIOID, 08. [] R. Senaratne and S. Halgamuge. Optimised landmark model matching for face recognition. In AFGR, page 6, 06. [21] H. Tao and T. Huang. Connected vibrations: a modal analysis approach for non-rigid motion tracking. In CVPR, pages 735 7, [22] R. Valenti, N. Sebe, and T. Gevers. Facial expression recognition: A fully integrated approach. In ICIAPW, pages 125 1, 07. [23] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, volume 1, pages , 01. [24] D. Vukadinovic and M. Pantic. Fully automatic facial feature point detection using Gabor feature based boosted classifiers. In SMC, pages , 05. [25] L. Wiskott, J. Fellous, N. Krüger, and C. Von der Malsburg. Face recognition by elastic bunch graph matching. TPAMI, 19(7): , [26] H. X. L. S. Z. H. C. Q. Yan, S. Face alignment using view-based direct appearance models. Intl J. Imaging Systems and Technology, 13:6 112, 03. [27] Z. Zeng, M. Pantic, G. Roisman, and T. Huang. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. TPAMI, pages 39 58, 09. [28] X. Zhu, J. Fan, and A. Elmagarmid. Towards facial feature extraction and verification for omni-face detection in video images. Image Processing, 2: , 02. 8