Early detection of locomotion intention is highly relevant to the development of intelligent rehabilitation/assistive robotics. While surface electromyography(sEMG) has been a promising tool, it is often challenged by the shear variability of sEMG patterns in contrast to only a handful of sEMG training samples per discrete motion intention class for each individual user to begin with. To address this issue, we introduce a deep convolutional generative adversarial networks (DCGANs), including dynamic time warping (DTW) and fast Fourier transform mean square error (FFT MSE) for artificial signal quality assessment. On a preliminary sEMG data set of 3-class directional lower-limb movement, the proposed method yielded an average accuracy rate of$89.31 \% \pm 6.52$. While this is a feasibility study using healthy human subjects only, the result warrants extended study to further establish the generative adversarial network learning for EMG intention detection in real-world rehabilitation/assistive system applications.

The olfactory system is known to be associated with emotion during odor stimulation. A well-designed computational model that can correctly recognize preference induced by odor stimulation can be vital in the food and perfume industries. Electroencephalogram (EEG) can be used to study the brain's response to odor stimulation due to its good temporal resolution and low acquisition cost. In this study, we proposed a novel self-attention deep learning framework: Temporal Segment Attention Network (TESANet) to classify the brain state of subjects when they are exposed to pleasant and unpleasant odors. Odor stimulation is a continuous process, the temporal dynamics of the EEG signal should reflect the continuous changes of the brain responses to the given odor, thus we design the model to capture the intercorrelation between time segments of the EEG by utilizing the self-attention mechanism. TESANet consists of a filter-bank layer to extract spectral features, a spatial convolution layer to extract spatial features, a temporal segmentation layer to split the data into overlapping time windows, a Long Short-Term Memory (LSTM) layer to encode the temporal segments, a self-attention layer to decode the temporal dynamics by learning the intercorrelation between time segments, and finally a fully connected layer for classification. Experiments on an olfactory EEG dataset demonstrated that the proposed method outperforms other competing deep learning methods for odor pleasantness classification.

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple linear transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

The non-stationarity inherent across sessions recorded on different days poses a major challenge for practical electroencephalography (EEG)-based Brain Computer Interface (BCI) systems. To address this issue, the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel approach to compute the variations between labelled training data and a batch of unlabelled test data based on the geodesic-distance of the discriminative subspaces of EEG data on the Grassmann manifold. Subsequently, spatial filters can be updated and features that are invariant against such variations can be obtained using a subset of training data that is closer to the test data. Experimental results show that the proposed adaptation method yielded improvements in classification performance.

We developed and studied a Steady-State Visual Evoked Potential (SSVEP) based BCI system using a high frequency visual stimuli (>25Hz) design for reducing visual fatigue. Existing SSVEP based BCI designs primarily use low frequency visual stimuli (<;20Hz) for eliciting relatively higher SSVEP signal, while the low frequency stimuli can provocate photosensitivity epileptic seizure. On the other hand, high frequency stimuli are visually more comfortable and cause less visual fatigue and seizure. To detect the weak high frequency SSVEP signal, we used multi-channel EEG and introduced canonical correlation analysis to identify the elicited SSVEP frequency. We designed and built a 30-character SSVEP BCI speller system without calibration and evaluated the performance metrics including classification accuracy and subjective fatigue ratings, in both high-frequency and low frequency SSVEP modes. The result indicates that the high frequency stimuli system archieved higher classification accuracy (averaged 80% in the 30-class classification) comparable to that by the low frequency system. Moreover, no subjects rated the visual feeling as unacceptable or uncomfortable with the high frequency system.

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

This paper addresses the key issue of discriminative feature extraction of electroencephalogram (EEG) signals in brain-computer interfaces. Recent advances in neuroscience indicate that multiple brain regions can be activated during motor imagery. The signal propagation among the regions can give rise to spurious effects in identifying event-related desynchronization/synchronization for discriminative motor imagery detection in conventional feature extraction methods. Particularly, we propose that computational models which account for both signal propagation and volume conduction effects of the source neuronal activities can more accurately describe EEG during the specific brain activities and lead to more effective feature extraction. To this end, we devise a unified model for joint learning of signal propagation and spatial patterns. The preliminary results obtained with real-world motor imagery EEG data sets confirm that the new methodology can improve classification accuracy with statistical significance.

The performance of Brain-Computer Interface (BCI) applications are sometimes hindered by non-stationarity in the EEG data from sessions on different days. This paper proposes an algorithm for adaptive training of a SVM classifier to address the non-stationarity in EEG by adapting the kernel to data from subsequent sessions. The kernel width parameter of the kernel function of the SVM classifier is adapted using an information theoretic cost function based on minimum error entropy (MEE). An experiment is performed using the proposed method on EEG data collected without feedback from 12 healthy subjects in two sessions on separate days. The results using the proposed method yielded a mean accuracy of 75%, which is significantly better compared to the baseline result of 67% without kernel adaptation (P=0.00029).

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Video-based Face Recognition (VFR) can be converted to the matching of two image sets containing face images captured from each video. For this purpose, we propose to bridge the two sets with a reference image set that is well-defined and pre-structured to a number of local models offline. In other words, given two image sets, as long as each of them is aligned to the reference set, they are mutually aligned and well structured. Therefore, the similarity between them can be computed by comparing only the corresponded local models rather than considering all the pairs. To align an image set with the reference set, we further formulate the problem as a quadratic programming. It integrates three constrains to guarantee robust alignment, including appearance matching cost term exploiting principal angles, geometric structure consistency using affine invariant reconstruction weights, smoothness constraint preserving local neighborhood relationship. Extensive experimental evaluations are performed on three databases: Honda, MoBo and YouTube. Compared with competing methods, our approach can consistently achieve better results.

The injection of emotional intelligence in human-computer interfaces is necessary for computer applications to appear intelligent when interacting with people. With the recent development of brain imaging techniques and brain-computer interfaces, computers can actually take a look inside users' head to observe their emotional states. This paper presents an EEG-based emotion detection system which detects emotional states based on short EEG segments of 1s. A novel feature extraction algorithm termed asymmetric spatial filtering is proposed to extract features from high dimensional EEG data. The effectiveness of the proposed method is tested for two types of emotion detection problems on data from five subjects.

This paper investigates how to apply active learning for the classification of motor imagery electroencephalography (EEG) signals to boost the performance for small training size. A new criterion is proposed to select the most representative and informative queries. The candidates are firstly chosen from the samples close to the center of the cluster that has the highest impurity of classes. A predefined number of such candidates and classifiers are forwardly buffered. Subsequently, the query is chosen such that the buffered classifiers can backward maximize the classification errors on labeled data. Experimental results conducted on the BCI competition IV data set IVb show the superior performance of the proposed active learning scheme, which is on average 5.12% higher in accuracy than that of the passive method by choosing the training size from 28 to 112.

Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (SSLDA), where the projecting vectors are not only constrained to sparsity but also structured with a pre-specified set of shapes. While the sparse priors deal with small sample size problem, the proposed structure regularization can also encode higher-order information with better interpretability. We also propose a simple and efficient optimization algorithm to solve the proposed optimization problem. Experiments on face images show the benefits of the proposed structured sparse LDA on both classification accuracy and interpretability.

Feature extraction has been a crucial and challenging task for EEG-based BCI applications mainly due to the problems of high-dimensionality and high noise level of EEG signals. In this paper we developed a novel feature extraction algorithm for EEG-based emotion detection problem. The proposed algorithm is derived from viewing EEG signals as the activation/deactivation of sources specific to the brain activities of interest. For binary classification problem, to be more specific, we consider the EEG signals for the two types of brain activities as characterized by the activation/deactivation of two discriminatory sources in the brain, with one source activated and the other one deactivated for one particular type of brain activities. The proposed algorithm, termed Asymmetric Spatial Pattern (ASP), extracts pairs of spatial filters, with each filter corresponding to only one of the two sources. The idea of ASP is neurologically plausible for certain situations. For example, according to the valence hypothesis of emotion, the left hemisphere is more activated in positive emotions and the right hemisphere is more activated in negative emotions. The effectiveness of the proposed algorithm is confirmed by application to real data for two types of EEG-based emotion detection problems: arousal detection (strong v.s. calm), and valence detection (positive v.s. negative). Experimental results on the real data also show that some of the asymmetric spatial patterns by ASP are consistent with the current neurophysiological findings on brain emotion processing.

This paper addresses an important problem known as EEG non-stationarity in Brain-computer Interfacing. We propose a novel technique called Dynamically Weighted Classification with Clustering (DWCC), which explores hidden states in non-stationary EEG using a modified k-means clustering method by combining cosine distance measure and mutual information criterion. DWCC builds a set of classifiers, one for each pair of clusters from different classes. A dynamically-weighted classifier ensemble network is trained to combine the outputs of the classifiers, where we propose to dynamically assign the weight of a classifier for each test sample based on its distances to the cluster centres associated with the classifier. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<0.05) compared to the baseline result of 75.9% using a single classifier.

It is still a challenging work for face recognition to deal with changes due to long term aging. Other than the complicated method using aging models to compensate the aging effect on face recognition, we use the age estimation result as a clue for the database retrieval. The age difference of input image and registered database image is estimated, and then it is used to limit the range of database retrieval and weight the recognition score for sorting. We show that the age estimation results can be used to improve the face recognition accuracy when long term aging effects are necessary to be considered.

The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters to automatically select key temporal-spatial discriminative EEG characteristics and the Naïve Bayesian Parzen Window (NBPW) classifier using offline learning in EEG-based Brain-Computer Interfaces (BCI). However, it has yet to address the non-stationarity inherent in the EEG between the initial calibration session and subsequent online sessions. This paper presents the FBCSP that employs the NBPW classifier using online adaptive learning that augments the training data with available labeled data during online sessions. However, employing semi-supervised learning that simply augments the training data with available data using predicted labels can be detrimental to the classification accuracy. Hence, this paper presents the FBCSP using online semi-supervised learning that augments the training data with available data that matches the probabilistic model captured by the NBPW classifier using predicted labels. The performances of FBCSP using online adaptive and semi-supervised learning are evaluated on the BCI Competition IV datasets IIa and IIb and compared to the FBCSP using offline learning. The results showed that the FBCSP using online semi-supervised learning yielded relatively better session-to-session classification results compared against the FBCSP using offline learning. The FBCSP using online adaptive learning on true labels yielded the best results in both datasets, but the FBCSP using online semi-supervised learning on predicted labels is more practical in BCI applications where the true labels are not available.

The Filter Bank Common Spatial Pattern (FBCSP) algorithm constructs and selects subject-specific discriminative CSP features from a filter bank of spatial-temporal filters in a motor imagery brain-computer interface (MI-BCI). However, information from other types of features could be extracted and combined with CSP features to enhance the classification performance. Hence this paper proposes a Filter Bank Feature Combination (FBFC) approach and investigates the use of CSP and Phase Lock Value (PLV) features, where the latter measures the phase synchronization between the EEG electrodes. The performance of the FBFC using CSP and PLV features is evaluated on four-class motor imageries from the publicly available BCI Competition IV Dataset IIa. The experimental results showed that the proposed FBFC using CSP and PLV features yielded a significant improvement in cross-validation accuracies on the training data (p=0.008) and better session-to-session transfer accuracies to the evaluation data compared to the use of CSP features using the FBCSP algorithm. This motivates the research of FBFC using a battery of other features that could possibly benefit EEG-based BCIs and multi-modal BCI systems.

This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance ( ≥ 95% confidence level) in most cases.

Optimum linear transformation under mixture of zero-mean Gaussian conditions is an intriguing problem, especially in learning discriminative spatial components in motor imagery EEG for building brain computer interfaces. However, it is not well addressed in the past. In this paper, we study optimum linear transformation under mixture of zero-mean Gaussian. In particular, we formulate optimum transformation as a Bhattacharyya error bound minimization problem, and derive a numerical solution to estimate the bound from training samples. Based on the solution, we develop an algorithm for selecting optimum linear transformation. The proposed method is evaluated, in comparison with the state-of-the-art methods, using a publicly available data set of motor imagery EEG. The results attest to the superiority of the method for detecting motor imagery.

We propose a novel linear discriminant analysis method and demonstrate its superiority over existing linear methods. Based on information theory, we introduce a non-parametric estimate of mutual information with variable kernel bandwidth. Furthermore, we derive a gradient-based optimization algorithm for learning the optimal linear reduction vectors which maximizes the mutual information estimate. We evaluate the proposed method by running cross-validation on 2 data sets from the UCI repository, together with linear and nonlinear SVMs as classifiers. The result attests to the superority of the method over conventional LDA and its variant, aPAC.

In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc.

This paper presents a subject-independent EEG (Electroencephalogram) classification technique and its application to a P300-based word speller. Due to EEG variations across subjects, a user calibration procedure is usually required to build a subject-specific classification model (SSCM). We remove the user calibration through the boosting of a committee of weak classifiers learned from EEG of a pool of subjects. In particular, we ensemble the weak classifiers based on their confidence that is evaluated according to the classification consistency. Experiments over ten subjects show that the proposed technique greatly outperforms the supervised classification models, hence making P300-based BCIs more convenient for practical uses.

In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel Filter Bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

In this paper, an electroencephalogram (EEG)-based brain computer interface (BCI) is proposed for two dimensional cursor control. The horizontal and vertical movements of the cursor are controlled by mu/beta rhythm and P300 potential respectively. The main advantages of this system are: (i) two almost independent control signals are produced simultaneously; (ii) the cursor can be moved from a random position to another random position in a screen. These advantages have been demonstrated in our experiment and data analysis.

This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10 subjects performing word spelling in an oddball paradigm. The results are very positive: the adapted models with unlabeled data yield virtually the same classification accuracy as the conventional methods with labeled data. Therefore, it proves the feasibility of P300-based BCIs which can be applied directly to a new subject without training sessions.

The "Brainy Communicator" is a novel state-of-the-art Brain-Computer Interface (BCI) system developed by the Institute for Infocomm Research, Singapore. It allows users to interact with the environment by using just the brain signals. The primary objective of this work is to enhance the quality of life for the people with severe disabilities, by helping them regain some control and communication abilities.

In this paper we present a new scheme for brain signal processing and classification for electroencephalogram based brain-computer interfaces, by emphasizing the extraction of space-time-frequency feature as well as the combination of classifiers. In particular, we use wavelet packets as a time-frequency analysis tool and employ sparse component analysis to recover source components in the brain signals. We subsequently apply multi-class common spatial pattern filters to the signals and thus obtain important space-time-frequency features for discrimination. Furthermore, a Bayesian method is developed to boost the system, by combining multiple support vector machines in a probabilistic way. We have tested the proposed scheme on real multi-class motor imagery signals, and its efficacy has been demonstrated.

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.

Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.

Particle filtering is a stochastic approach to Bayesian recursive inference. In many computer vision applications with limited number of random samples, however, conventional particle filters may find it difficult to accurately represent the desired a posteriori distribution especially for target objects with narrow likelihood functions. This paper proposes a new particle filtering framework which, by incorporating a special indirect measurement model, can significantly improve the representation capability of the particle set, yielding an accurate estimation of a posteriori distribution for the purpose of tracking. In particular, an add-on resampling technique is proposed to incorporate the indirect measurement. In this way, we can alleviate the problem with large numbers of particles required in conventional particle filtering. Positive experimental results on both synthetic sequences and real world videos are obtained.

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

This paper addresses the problem of soft controlling an avatar's facial expressions by its user's facial expressions. For example, when a user laughs, the avatar also gives a laughing expression but the avatar does not necessarily mimic the user's expression exactly. The major challenge here is how to recognize the user's facial expressions in real time. Unlike conventional approaches trying to recognize universal expressions independent to individuals, we use a personalized scheme to recognize facial expressions. We represent facial movements by facial motion graph (FMG), which is based on feature points and their relations. Expression discrimination is achieved by analyzing the similarity between the new FMG and the person's FMG models of known expressions using continuous dynamic programming. Experiments show excellent accuracy and near real time speed of the system.