Face recognition via incremental 2DPCA

Recently, the Two-Dimensional Principal Component Analysis (2DPCA) model is proposed and proved to be an efficient approach for face recognition. In this paper, we will investigate the incremental 2DPCA and develop a new constructive method for incrementally adding observation to the existing eigen-space model. An explicit formula for incremental learning is derived. In order to illustrate the effectiveness of the proposed approach, we performed some typical experiments and show that we can only keep the eigen-space of previous images and discard the raw images in the face recognition process. Furthermore, this proposed incremental approach is faster when compared to the batch method (2DPCD) and the recognition rate and reconstruction accuracy are as good as those obtained by the batch method.<br /

Bags of Affine Subspaces for Robust Object Tracking

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces. Distances between subspaces are then obtained by exploiting the non-Euclidean geometry of Grassmann manifolds. Experiments on challenging videos (containing object occlusions, deformations, as well as variations in pose and illumination) indicate that the proposed method achieves higher tracking accuracy than several recent discriminative trackers.Comment: in International Conference on Digital Image Computing: Techniques and Applications, 201

Template update algorithms and their application to face recognition systems in the deep learning era

Biometric technologies and facial recognition systems are reaching a very high diffusion for authentication in personal devices and public and private security systems, thanks to their intrinsic reliability and user-friendliness. However, although deep learning-based facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, different facial expressions, different poses and lighting changes. In the last decade, several "adaptive" biometric systems have been proposed to deal with this problem. Unfortunately, adaptive methods usually lead to a growth of the system in terms of memory and computational complexity and involve the risk of inserting impostors among the templates. The first goal of this PhD thesis is the presentation of a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates. This classification-selection approach overcomes the problem of manual updating and stringent computational requirements. In the second part of the thesis, we analyzed if and to what extent this "optimized" self-updating strategy improves the facial recognition performance, especially in application contexts where the facial biometric trait undergoes great changes due to the passage of time. In contexts of long-term use, in fact, the high representativeness of the deep features may not be enough and this is usually overcome with a re-enrollment phase. For this reason, one of our goals was to evaluate how much an automatic template updating system could compete with human-in-the-loop in terms of performance. To simulate situations of long-term use in which the temporal variability of biometric data is high, we acquired a new dataset collected by using frames of some videos in YouTube related to Daily Photo Projects: people take a picture every day for a certain period of time, usually to show how their appearance is changing. The temporal information present in this new dataset allowed us to evaluate how long a facial feature can remain representative depending on the context and the recognition system. Extensive experiments on different datasets and using different facial features are conducted to define the contexts of applicability and the usefulness of adaptive systems in the deep learning era

A Distributed and Secure Algorithm for Computing Dominant SVD Based on Projection Splitting

In this paper, we propose and study a distributed and secure algorithm for computing dominant (or truncated) singular value decompositions (SVD) of large and distributed data matrices. We consider the scenario where each node privately holds a subset of columns and only exchanges ''safe'' information with other nodes in a collaborative effort to calculate a dominant SVD for the whole matrix. In the framework of alternating direction methods of multipliers (ADMM), we propose a novel formulation for building consensus by equalizing subspaces spanned by splitting variables instead of equalizing themselves. This technique greatly relaxes feasibility restrictions and accelerates convergence significantly, while at the same time yielding simple subproblems. We design several algorithmic features, including a low-rank multiplier formula and mechanisms for controlling subproblem solution accuracies, to increase the algorithm's computational efficiency and reduce its communication overhead. More importantly, the possibility appears remote, if possible at all, for a malicious node to uncover the data stored in another node through shared quantities available in our algorithm, which is not the case in existing distributed or parallelized algorithms. We present the convergence analysis results, including a worst-case complexity estimate, and extensive experimental results indicating that the proposed algorithm, while safely guarding data privacy, has a strong potential to deliver a cutting-edge performance, especially when communication costs are high

Incremental Sparse-PCA Feature Extraction For Data Streams

Intruders attempt to penetrate commercial systems daily and cause considerable financial losses for individuals and organizations. Intrusion detection systems monitor network events to detect computer security threats. An extensive amount of network data is devoted to detecting malicious activities. Storing, processing, and analyzing the massive volume of data is costly and indicate the need to find efficient methods to perform network data reduction that does not require the data to be first captured and stored. A better approach allows the extraction of useful variables from data streams in real time and in a single pass. The removal of irrelevant attributes reduces the data to be fed to the intrusion detection system (IDS) and shortens the analysis time while improving the classification accuracy. This dissertation introduces an online, real time, data processing method for knowledge extraction. This incremental feature extraction is based on two approaches. First, Chunk Incremental Principal Component Analysis (CIPCA) detects intrusion in data streams. Then, two novel incremental feature extraction methods, Incremental Structured Sparse PCA (ISSPCA) and Incremental Generalized Power Method Sparse PCA (IGSPCA), find malicious elements. Metrics helped compare the performance of all methods. The IGSPCA was found to perform as well as or better than CIPCA overall in term of dimensionality reduction, classification accuracy, and learning time. ISSPCA yielded better results for higher chunk values and greater accumulation ratio thresholds. CIPCA and IGSPCA reduced the IDS dataset to 10 principal components as opposed to 14 eigenvectors for ISSPCA. ISSPCA is more expensive in terms of learning time in comparison to the other techniques. This dissertation presents new methods that perform feature extraction from continuous data streams to find the small number of features necessary to express the most data variance. Data subsets derived from a few important variables render their interpretation easier. Another goal of this dissertation was to propose incremental sparse PCA algorithms capable to process data with concept drift and concept shift. Experiments using WaveForm and WaveFormNoise datasets confirmed this ability. Similar to CIPCA, the ISSPCA and IGSPCA updated eigen-axes as a function of the accumulation ratio value, forming informative eigenspace with few eigenvectors

Towards On-line Domain-Independent Big Data Learning: Novel Theories and Applications

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets

Online Geometric Human Interaction Segmentation and Recognition

The goal of this work is the temporal localization and recognition of binary people interactions in video. Human-human interaction detection is one of the core problems in video analysis. It has many applications such as in video surveillance, video search and retrieval, human-computer interaction, and behavior analysis for safety and security. Despite the sizeable literature in the area of activity and action modeling and recognition, the vast majority of the approaches make the assumption that the beginning and the end of the video portion containing the action or the activity of interest is known. In other words, while a significant effort has been placed on the recognition, the spatial and temporal localization of activities, i.e. the detection problem, has received considerably less attention. Even more so, if the detection has to be made in an online fashion, as opposed to offline. The latter condition is imposed by almost the totality of the state-of-the-art, which makes it intrinsically unsuited for real-time processing. In this thesis, the problem of event localization and recognition is addressed in an online fashion. The main assumption is that an interaction, or an activity is modeled by a temporal sequence. One of the main challenges is the development of a modeling framework able to capture the complex variability of activities, described by high dimensional features. This is addressed by the combination of linear models with kernel methods. In particular, the parity space theory for detection, based on Euclidean geometry, is augmented to be able to work with kernels, through the use of geometric operators in Hilbert space. While this approach is general, here it is applied to the detection of human interactions. It is tested on a publicly available dataset and on a large and challenging, newly collected dataset. An extensive testing of the approach indicates that it sets a new state-of-the-art under several performance measures, and that it holds the promise to become an effective building block for the analysis in real-time of human behavior from video