Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Deep learning of fuzzy weighted multi-resolution depth motion maps with spatial feature fusion for action recognition

Human action recognition (HAR) is an important yet challenging task. This paper presents a novel method. First, fuzzy weight functions are used in computations of depth motion maps (DMMs). Multiple length motion information is also used. These features are referred to as fuzzy weighted multi-resolution DMMs (FWMDMMs). This formulation allows for various aspects of individual actions to be emphasized. It also helps to characterise the importance of the temporal dimension. This is important to help overcome, e.g., variations in time over which a single type of action might be performed. A deep convolutional neural network (CNN) motion model is created and trained to extract discriminative and compact features. Transfer learning is also used to extract spatial information from RGB and depth data using the AlexNet network. Different late fusion techniques are then investigated to fuse the deep motion model with the spatial network. The result is a spatial temporal HAR model. The developed approach is capable of recognising both human action and human–object interaction. Three public domain datasets are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the art algorithms

Non-Gaussian data modeling with hidden Markov models

In 2015, 2.5 quintillion bytes of data were daily generated worldwide of which 90% were unstructured data that do not follow any pre-defined model. These data can be found in a great variety of formats among them are texts, images, audio tracks, or videos. With appropriate techniques, this massive amount of data is a goldmine from which one can extract a variety of meaningful embedded information. Among those techniques, machine learning algorithms allow multiple processing possibilities from compact data representation, to data clustering, classification, analysis, and synthesis, to the detection of outliers. Data modeling is the first step for performing any of these tasks and the accuracy and reliability of this initial step is thus crucial for subsequently building up a complete data processing framework. The principal motivation behind my work is the over-use of the Gaussian assumption for data modeling in the literature. Though this assumption is probably the best to make when no information about the data to be modeled is available, in most cases studying a few data properties would make other distributions a better assumption. In this thesis, I focus on proportional data that are most commonly known in the form of histograms and that naturally arise in a number of situations such as in bag-of-words methods. These data are non-Gaussian and their modeling with distributions belonging the Dirichlet family, that have common properties, is expected to be more accurate. The models I focus on are the hidden Markov models, well-known for their capabilities to easily handle dynamic ordered multivariate data. They have been shown to be very effective in numerous fields for various applications for the last 30 years and especially became a corner stone in speech processing. Despite their extensive use in almost all computer vision areas, they are still mainly suited for Gaussian data modeling. I propose here to theoretically derive different approaches for learning and applying to real-world situations hidden Markov models based on mixtures of Dirichlet, generalized Dirichlet, Beta-Liouville distributions, and mixed data. Expectation-Maximization and variational learning approaches are studied and compared over several data sets, specifically for the task of detecting and localizing unusual events. Hybrid HMMs are proposed to model mixed data with the goal of detecting changes in satellite images corrupted by different noises. Finally, several parametric distances for comparing Dirichlet and generalized Dirichlet-based HMMs are proposed and extensively tested for assessing their robustness. My experimental results show situations in which such models are worthy to be used, but also unravel their strength and limitations