Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.

The majority of current facial age estimation methods are based on appearance based features. However, wrinkle based research has not been widely addressed. In this paper, we propose a novel method based on multi-scale aging patterns (MAP). These directly extract the features from local patches without extensive geometric modelling. First, we locate facial landmarks by using the Face++ detector and then normalize the face by using a linear transformation. We define a face template which consists of ten predefined wrinkle regions. Then, for each region, we detect wrinkles and construct aging patterns by using the MAP. Finally, the age is estimated by implementing the sequential minimal optimization (SMO). The performance of the algorithms is assessed by using mean absolute error (MAE) on the benchmark database - FERET. We observe that MAP produces a lower MAE of 4.87 on FERET compared to the benchmark algorithms. Therefore, we conclude that wrinkle could be used as a feature on face age estimation. Future work would involve improvements of the algorithm by combining other descriptors such as non-wrinkle descriptor and appearance parameters.

The assessment of embryos in vitro is an important tool both in In Vitro Fertilization (IVF) treatment and for research purposes. Traditionally, such assessment has been done manually and demands extensive training and expertise. Interobserver variability limits the use of evaluation, and the manual labor increases the cost of treatment and research. To this end, feature extraction and automatic annotation using computer aided tools would both improve objectivity and save time. Due to the sensitivity of embryos, any evaluation must be performed using non-invasive imaging techniques, which sets a limit for the ability to artificially enhance image contrast, and computer aided assessment of microscopic images under clinical conditions also poses a number of difficulties due to the high level of noise and the unpredictable imaging environment. To overcome this, we propose a framework using a Hough Transform (HT) for the extraction of circular shapes in microscopic embryo images, and also use the algorithm for successful automatic profiling of the pronuclear breakdown (PNB) at syngami and detection of its timing. The best results are achieved when using a HT for embryo outline detection in combination with image variance of the embryo interior. Using our method, we achieve an overall accuracy of 95.0% detecting the position of the embryo outline, and 83.0% detecting the timing of the PNB.

Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realize facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents an evaluation of person identity verification using facial video data, organized in conjunction with the International Conference on Biometrics (ICB 2009). It involves 18 systems submitted by seven academic institutes. These systems provide for a diverse set of assumptions, including feature representation and preprocessing variations, allowing us to assess the effect of adverse conditions, usage of quality information, query selection, and template construction for video-to-video face authentication.

For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, low-dimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to be recovered, but is both difficult and expensive. This paper explores a potential middle ground between these approaches, using the hierarchical Gaussian process latent variable model to learn activity at different hierarchical scales within the human skeleton. We show that by training on full-body activity data then descending through the hierarchy in stages and exploring subtrees independently of one another, novel poses may be recovered. Experimental results on motion capture data and monocular video sequences demonstrate the utility of the approach, and comparisons are drawn with existing low-dimensional activity models. © 2009. The copyright of this document resides with its authors.

In this paper, we present a sequential framework using facial motion information as a subsidiary to improve face recognition performance. As is generally known, reasonable static face recognition has been achieved based on subspace reduction techniques. In order to further improve performance, some extra cues, such as temporal variation, are investigated by building dynamic models. We propose a permuted similarity motion fea-ture and integrate it into a sequential recognition system. This system can select the best candidate from the Rank-N candidates picked up in the recognition step based on static appearance parameters by using motion information. The recognition rate of the motion similarity is compared with the motion feature obtained from auto-regressive models to prove its efficiency. In addition, the sequential system achieves better performance when the motion information is integrated with the static appearance information in a flexible manner. 1

We present a method for tracking human activity using observations from a moving narrow-baseline stereo camera. Range data are computed from the disparity between stereo image pairs. We propose a novel technique for calculating weighting scores from range data given body configuration hypotheses. We use a modified Annealed Particle Filter to recover the optimal tracking candidate from a low dimensional latent space computed from motion capture data and constrained by an activity model. We evaluate the method on synthetic data and on a walking sequence recorded using a moving hand-held stereo camera. 1

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guide sampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-by-synthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth motion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower absolute and relative error scores for both monocular and 2-camera sequences using 80% fewer particles.

We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a minimum description length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.

This paper presents an approach to human motion tracking using multiple pre-trained activity models for propagation of particles in Annealed Particle Filtering. Hidden Markov models are trained on dimensionally reduced joint angle data to produce models of activity. Particles are divided between models for propagation by HMM synthesis, before converging on a solution during the annealing process. The approach facilitates multi-view tracking of unknown subjects performing multiple known activities with low particle numbers.

This paper addresses the issues associated with performing feature or parameter selection for non-linear classifiers using a basis pursuit regularization framework. New results on representing the feature selection problem as a primal/dual calculation for both hard and soft margin classification problems are derived, and it is shown that optimal feature selection can be posed, in dual form, as a set of 2n linear inequality constraints. While this is efficient, it does limit the technique to non-linear kernels that have a finite expansion, such as polynomials. The issues associated with both efficiently calculating a polynomial basis pursuit classifier are then addressed and the technique is shown to improve discrimination performance on the MNIST digit set. 1 Introduction to Feature Selection Feature and parameter selection is an important part of many machine learning problems. It can be used to identify important terms when the problem is poorly structured and thus learn more about the variables ’ information content. Similarly, it can be used to build

When attempting to code faces for modelling or recognition, estimates of dimensions are typically obtained from an ensemble. These tend to be significantly sub-optimal. Each face contains both predictable and non-predictable qualities

When attempting to code faces for modeling or recognition, estimates of di-mensions are typically obtained from an ensemble. These tend to be signif-icantly sub-optimal. Firstly, ensembles are rarely balanced with regard to identity and expression. This can be overcome by dividing the ensemble by type of variation and rotating sub-spaces relative to one another. Secondly, each face contains both predictable and non-predictable qualities; only the predictable aspects are useful for defining coding systems for other faces. Variance-based methods of defining codes (PCA) will provide eigenvectors which are themselves potential faces. Predictable aspects will induce eigen-vectors with comparable levels of spatial redundancy to the ensemble. We show that this gives relatively short and consistent codes, and allows fast and accurate fitting of codes to faces. 1

Facial variation divides into a number of functional subspaces, and ensemble-specific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectation-maximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.

Facial variation divides into a number of functional subspaces. An improved method of measuring these was designed within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity, expression) were obtained by Principal Components Analysis on appropriate groups of faces. An iterative algorithm was applied to image codings to maximise the probability of coding across these non-orthogonal subspaces before obtaining the projection on each sub-space and recalculating the spaces. This procedure enhances identity recognition, reduces overall sub-space variance and produces Principal Components with greater span and less contamination.

We describe results obtained from a testbed used to investigate different codings for automatic face recognition. An eigenface coding of shape-free faces using manually located landmarks was more effective than the corresponding coding of correctly shaped faces. Configuration also proved an effective method of recognition, with rankings given to incorrect matches relatively uncorrelated with those from shape-free faces. Both sets of information combine to improve significantly the performance of either system. The addition of a system, which directly correlated the intensity values of shape-free images, also significantly increased recognition, suggesting extra information was still available. The recognition advantage for shape-free faces reflected and depended upon high-quality representation of the natural facial variation via a disjoint ensemble of shape-free faces; if the ensemble comprised nonfaces, a shape-free disadvantage was induced. Manipulation within the shape-free coding to emphasize distinctive features of the faces, by caricaturing, allowed further increases in performance; this effect was only noticeable when the independent shape-free and configuration coding was used. Taken together, these results strongly support the suggestion that faces should be considered as lying in a high-dimensional manifold, which is locally linearly approximated by these shapes and textures, possibly with a separate system for local features. Principal components analysis is then seen as a convenient tool in this local approximation.

We derive a machine-based face recognition system theoretically which is similar to many practical ones and show its behaviour has features of the human system. We reproduce the caricature effect, show that human and machine-based similarity and distinctiveness are connected and confirm machine based predictions of typicality with human data. Finally we draw lessons for machine-based recognition.