Race classification using gaussian-based weight K-nn algorithm for face recognition

One of the greatest challenges in facial recognition systems is to recognize faces around different race and illuminations. Chromaticity is an essential factor in facial recognition and shows the intensity of the color in a pixel, it can greatly vary depending on the lighting conditions. The race classification scheme proposed which is Gaussian based-weighted K-Nearest Neighbor classifier in this paper, has very sensitive to illumination intensity. The main idea is first to identify the minority class instances in the training data and then generalize them to Gaussian function as concept for the minority class. By using combination of K-NN algorithm with Gaussian formula for race classification. In this paper, image processing is divided into two phases. The first is preprocessing phase. There are three preprocessing comprises of auto contrast balance, noise reduction and auto-color balancing. The second phase is face processing which contains six steps; face detection, illumination normalization, feature extraction, skin segmentation, race classification and face recognition. There are two type of dataset are being used; first FERET dataset where images inside this dataset involve of illumination variations. The second is Caltech dataset which images side this dataset contains noises

Threshold Based Skin Color Classification

In this paper, we presented a new formula for skin classification. The proposed formula can overcome sensitivity to noise. Our approach was based multi-skin color Hue, Saturation, and Value color space and multi-level segmentation. Skin regions were extracted using three skin color classes, namely the Caucasoid, Mongolid and Nigroud. Moreover, in this formula, we adopted Gaussian-based weight k-NN algorithm for skin classification. The experiment result shows that the best result was achieved for Caucasoid class with 84.29 percent fmeasure

Pattern recognition techniques: Studies on appropriate classifications

Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supervised. Supervised pattern recognition methods are utilized in the examination of various sources' chemical data such as sensor measurements, spectroscopy, and chromatography. The unsupervised classification techniques use algorithms to classify and analyze huge amounts of raster cells. Semi-Supervised Learning is an approach that is in the middle ground between supervised and unsupervised learning and guarantees to be better at classification by involving data that is unlabeled. In this paper, we tried to categories pattern recognition methods and explain about each of them and we compared supervised method with unsupervised method in terms of types and location of features. INTRODUCTION Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supervised. This is dependent on the analyst's intention of the information that needs to be utilized or that is available regarding the samples comprising of the data matrix. In the supervised methods, or the classification method, prior description is made on the classes as the concept or the attribute used to classify the samples into subsets are already known [1]. In the unsupervised method, the classification is removed by considering only the variations and resemblances among the samples, without utilizing any of their details. The semi-supervised method is in the middle ground between the supervised and unsupervised analysis and assures to be a better classification using the non-labeled details

A brief review of mobile cloud computing opportunities

Cloud Computing (CC) is becoming popular as the latest technology in the infrastructure of the computing world. The CC allows the users to make use of the resources when needed. Mobile Cloud Computing (MCC) combines the cloud computing concept into a mobile setting and manages to deter barriers associated with performance of the mobile devices. However, this does not mean that there are no problems with these new advantages. MCC encounter some common security issues include personal data management, privacy, identity authentication, and potential attacks. The security problems are major obstacle in the mobile cloud computing paradigm’s swift adaptability. This study initially provides the background to MCC, which includes definitions, infrastructure and its applications. Furthermore, the different advantages of MCC will be highlighted in the study

Autocalibration of Outlier Threshold with Autoencoder Mean Probability Score

Anomaly detection is a widely studied field in computer science with applications ranging from intrusion and fraud detection, medical diagnosis and quality assurance in manufacturing. The underlying premise is that an anomaly is an observation that doesn\u27t conform to what is considered to be normal. A problem however is in defining the threshold that draws the line between what is normal and what is an anomaly which is largely dependent on a domain expert or from empirical testing that would yield the best result. Another problem is that the availability of data with regards to what is not normal is highly unavailable in real world scenarios making it difficult for traditional machine learning techniques to build a classification model. In this study, we propose a method that automatically determines the outlier threshold using a semi-supervised learning approach with autoencoders. To validate the performance of our proposed approach, we perform several experiments in comparison with traditional outlier detection approaches as well as an existing semi-supervised approach in one class classification, specifically OneClassSVM. The goal of this study is to eventually apply the method for autocalibration of anomaly detection of frames in video sequences. Initial results are also presented in a computer vision task