Interactive feature learning framework for mask-occluded face recognition

Microarray technology has generated vast amounts of gene expression data with distinct patterns. Based on the premise that genes of correlated functions tend to exhibit similar expression patterns, various machine learning methods have been applied to capture these specific patterns in microarray data. However, the discrepancy between the rich expression profiles and the limited knowledge of gene functions has been a major hurdle to the understanding of cellular networks. To bridge this gap so as to properly comprehend and interpret expression data, we introduce Relevance Feedback to microarray analysis and propose an interactive learning framework to incorporate the expert knowledge into the decision module. In order to find a good learning method and solve two intrinsic problems in microarray data: high dimensionality and small sample size, we also propose a semi-supervised learning algorithm: Kernel Discriminant-EM (KDEM). This algorithm efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in Discriminant-EM (DEM) to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Relevance Feedback technique and KDEM together construct an efficient and effective interactive semi-supervised learning framework for microarray analysis. Extensive experiments on the yeast cell cycle regulation data set and Plasmodium falciparum red blood cell cycle data set show the promise of this approach.

The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework.

One of the biggest challenges in intelligent robotics is to build robots that can understand and use language. Such robots will be a part of our everyday life; at the same time, they can be of great help to investigate the complex mechanism of language acquisition by infants in constructive approach. To this end, we think that the practical long-term on-line concept/word learning algorithm for robots and the interactive learning framework are the key issues to be addressed. In this paper we develop a practical on-line learning algorithm that solves three remaining problems in our previous study. We also propose an interactive learning framework, in which the proposed on-line learning algorithm is embedded. The main contribution of this paper is to develop such a practical learning framework, and we test it on a real robot platform to show its potential toward the ultimate goal.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.

Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.

Early diagnosis of Alzheimer's disease (AD) allows individuals and their health managers to manage healthier medication. We proposed an approach for classification of AD stages, with respect to principal component analysis (PCA)-based algorithm. The PCA has been extensively applied as the most auspicious face-recognition algorithm. For the proposed algorithm, 100 images of 10 children were transformed for feature extraction and covariance matrix was constructed to obtain eigenvalues. The eigenvector provided a useful framework for face recognition. For the classification of AD stages, magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) data were obtained from Alzheimer's Disease Neuroimaging Initiative database. Hippocampus is one of the most affected regions by AD. Thus, we selected clusters of voxels from the "hippocampus" of AD screening stage (mild cognitive impairment), AD stage 1, AD stage 2, and AD stage 3. By using eigenvectors corresponding to maximum eigenvalues of fMRI data, the purposed algorithm classified the voxels of AD stages effectively.

COACH (COrrective Advice Communicated by Humans), a new interactive learning framework that allows non-expert humans to shape a policy through corrective advice, using a binary signal in the action domain of the agent, is proposed. One of the main innovative features of COACH is a mechanism for adaptively adjusting the amount of human feedback that a given action receives, taking into consideration past feedback. The performance of COACH is compared with the one of TAMER (Teaching an Agent Manually via Evaluative Reinforcement), ACTAMER (Actor-Critic TAMER), and an autonomous agent trained using SARSA(?) in two reinforcement learning problems. COACH outperforms all other learning frameworks in the reported experiments. In addition, results show that COACH is able to transfer successfully human knowledge to agents with continuous actions, being a complementary approach to TAMER, which is appropriate for teaching in discrete action domains.

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and attributes. In this paper, we consider the following challenging multi-label stream scenario: the new labels emerge continuously in the changing environments, and are assigned to the previous data. In this setting, data mining solutions must be able to learn the new concepts and avoid catastrophic forgetting simultaneously. We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels. We utilize the knowledge from the previous tasks to learn new knowledge to solve the current task. Then, the system compresses historical and novel knowledge and preserves it while waiting for new emerging tasks. CIFDM consists of three components: 1) a knowledge bank that stores the existing feature-level compressed knowledge, and predicts the observed labels so far; 2) a pioneer module that aims to learn and predict new emerged labels based on knowledge bank.; 3) an interactive knowledge compression function which is used to compress and transfer the new knowledge to the bank, and then apply the current compressed knowledge to initialize the label embedding of the pioneer for the next task.

In this paper, a novel framework for face recognition is developed by using adaptive binning and adaboost technique. Adaptive binning is an efficient classifier technique to classify the object and the results are represented in Histogram Gabor Phase Pattern [HGPP]. The resultant HGPP is again applied with an adaboost classification technique to improve the efficiency of the pattern by further reducing the computational complexity. This new framework is experimentally verified with FERET and found that the recognition rate of the system is improved. The main feature of this system is a unified model for assessing all the probe sets of the face images and best results are thus achieved.

Extraction of global face appearance and local interior differences is essential for any face recognition application. This paper presents a novel framework for face recognition by combining two effective descriptors namely, Zernike moments (ZM) and histogram of oriented gradients (HOG). ZMs are global descriptors that are invariant to image rotation, noise and scale. HOGs capture local details and are robust to illumination changes. Fusion of these two descriptors combines the merits of both local and global approaches and is effective against diverse variations present in face images. Further, as the processing time of HOG features is high owing to its large dimensionality, so, the study proposes to improve its performance by selecting only most discriminative HOG features (named discriminative HOG (DHOG)) for performing recognition. Efficacy of the proposed methods (DHOG, [Formula: see text] and [Formula: see text]) is tested on ORL, Yale and FERET databases. DHOG provides an improvement of 3% to 5% over the existing HOG approach. Recognition results achieved by [Formula: see text] and [Formula: see text] are up to 15% and 18% higher respectively than those obtained with these descriptors individually. Performance is also analyzed on LFW face database and compared with recent and state-of-the-art methods.

In recent years considerable progress has been made by the researchers in the field of pattern recognition in general and face recognition in particular. Computers can now outperform human brain in face recognition and verification tasks. While most of the methods related to face recognition perform well under specific conditions, some show anomalous behavior when the degree of accuracy is concerned. In this paper, we have divided the face recognition task into three sub-parts as Segmentation, Feature Extraction, and Classification. Information from face image is extracted and modelled using Eigenvectors. The weights calculated from Eigenvectors are classified by the statistical classifier using distance metric specification. The system is capable of recognition to an accuracy of 96%, having a standard deviation of 0.662 for facial expression variations.

COACH (COrrective Advice Communicated by Humans) is an interactive learning framework that allows non-expert humans to shape a policy through corrective advice, using a binary signal in the action domain of the agent. The original COACH formulation has been tested in problems of one-dimensional actions spaces with RBF linear models for the policy approximation. In this paper the COACH framework is tested with two more complex learning problems of more than one dimension in the action domain such as learning to drive a bicycle, and ball-dribbling with humanoid robots. Moreover, for the second problem, the COACH's principles are extended for training a decision-making system using a fuzzy based policy approximation. In these two problems the performance of COACH is compared with the one of other learning methods, obtaining better results. Results show that COACH is able to transfer successfully human knowledge to agents with multi-dimensional continuous action domains based on the use of different kind of models.

A support vector machine-based multi-view face detection and recognition framework is described. Face detection is carried out by constructing several detectors, each of them in charge of one specific view. The symmetrical property of face images is employed to simplify the complexity of the modelling. The estimation of head pose, which is achieved by using the support vector regression technique, provides crucial information for choosing the appropriate face detector. This helps to improve the accuracy and reduce the computation in multi-view face detection compared to other methods. For video sequences, further computational reduction can be achieved by using a pose change smoothing strategy. When face detectors find a face in frontal view, a support vector machine-based multi-class classifier is activated for face recognition. All the above issues are integrated under a support vector machine framework. Test results on four video sequences are presented, among them the detection rate is above 95%, recognition accuracy is above 90%, average pose estimation error is around 10/spl deg/, and the full detection and recognition speed is up to 4 frames/second on a Pentium II 300 PC.

This paper studies face recognition and person-specific face image retrieval in unconstrained environments. The proposed method consists of two parts: offline and online learning. In offline stage, we take advantage of both global and local features in a Bayesian framework for generic face recognition. In online stage, the offline learned classifier is adapted according to the query images of a given person, from which a person-specific face image retriever can be obtained. Our method is applied to the ldquolabeled faces in the wildrdquo dataset, which is more realistic than usual face recognition datasets. We show that the combination of offline and online learning can yield very promising results.

A framework for pose-invariant face recognition using the pose alignment method is described in this paper. The main idea is to normalize the face view in depth to frontal view as the input of face recognition framework. Concretely, an inputted face image is first normalized using the irises information, and then the pose subspace algorithm is employed to perform the pose estimation. To model the pose-invariance, the face region is divided into three rectangles with different mapping parameters in this pose alignment algorithm. So the affine transformation parameters associated with the different poses can be used to align the input pose image to frontal view. To evaluate this algorithm objectively, the views after the pose alignment are incorporated into the frontal face recognition system. Experimental results show that it has the better performance and it increases the recognition rate statistically by 17.75% under the pose that rotated within 30 degree.