Some Information Geometric Aspects of Cyber Security by Face Recognition

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-29, pub-electronic 2021-07-09Publication status: PublishedSecure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Robust Watershed Segmentation Using The Wavelet Transform

The watershed transform has been used for image segmentation relying mostly on image gradients

Face Recognition Based on Texture Discrimination by Using Geodesic Distance Approximations Between Multivariate Normal Distributions

Geodesic distances are a natural dissimilarity measure between probability distributions of a fixed type, and are used to discriminate texture in several image-based measurements. Besides, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to discriminate textures in the context of face recognition. Unlike the typical appearance-based approach that uses low-resolution grayscale face images, we propose a novel generative approach for face recognition based on texture discrimination. In the proposed approach, sparse facial features are extracted from high-resolution color face images using predefined landmark topologies, in which landmarks are in discriminative locations of face images. By adopting a common landmark topology, the dissimilarity between distinct face images can be scored in terms of the dissimilarities between the texture in their corresponding landmark vicinities. The proposed multivariate normal distributions represent the color intensities around each landmark location. The classification of new face samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score. The proposed face recognition method was compared to methods representative of the state-of-the-art using color and grayscale face images, and presented higher recognition rates. Moreover, the proposed measures to discriminate textures tend to be efficient in face recognition and in general texture discrimination (e.g., texture recognition of material images), as our experiments sugges