Fast Gender Recognition by Using a Shared-Integral-Image Approach

[[abstract]]We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results.[[conferencetype]]國際[[conferencedate]]20090419~20090424[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Classifiaction algorithms for face-based identification systems

Práce se zabývá rešerší klasifikačních algoritmů pro identifikaci osob podle obličeje. Cílem práce je implementace algoritmů do existujícího systému pro rozpoznávání obličejů a vyhodnocení vlivu jednotlivých klasifikátorů. Na základě provedené rešerše byly k implementaci vybrány následující klasifikátory: algoritmus k - nejbližších sousedů (K-NN), metoda podpůrných vektorů (SVM) a neuronové sítě (NN). Tyto klasifikační algoritmy byly implementovány v jazyce C++ s využitím open source knihovny OpenCV. Dále byla představena snímková databáze IFaVID a testovací metodologie implementovaných algoritmů.The thesis deals with the research of classification algorithms for face-based identification. The aim is to implement algorithms into an existing system for face recognition and the evaluation of the impect of individual classifiers. According to the survey of face recognition methods the following classifiers were chosen for implementation: K - Nearest Neighbours (K-NN), Support Vector Machines (SVM) and the Neural Networks. These classification algorithms were implemented in C++ (Microsoft Visual Studio 2010) using the open source library OpenCV. Furthermore, the IFaVID database and the methodology used to test the implemented algorithms were introduced.

Hair Color Classification in Face Recognition using Machine Learning Algorithms

Security through automatic human identification is critically important today, and this is largely due to the high volume of communications. Most methods used to identify individuals often use biometrics information, such as facial characteristics. Therefore, face recognition and classification have garnered great interest among computer vision researchers over the past decade. This pattern recognition problem is divided into several subcategories, such as eye or hair detection and classification. Hair is a salient feature in the human face and is one of the most important cues in face detection and recognition. Accurate detection and presentation of the hair region is one of the key components in the automatic synthesis of human facial caricature. In this work, hair color classification through feature extraction and machine learning methods was performed. The impacts of different features and classifiers were investigated using color samples. Support vector machines (SVM) and Kth nearest neighbors (K-NN) were trained by variety sets of statistical and color features, and the trained models were validated. Additionally, the effects of the size of datasets and feature dimensionality reduction were obtained. The best accuracy rate of 99% was achieved through a support vector machine with radial basis kernel function (SVM-RBF) using nine selected statistical and color features

A Swarm intelligence approach for biometrics verification and identification

In this paper we investigate a swarm intelligence classification approach for both biometrics verification and identification problems. We model the problem by representing biometric templates as ants, grouped in colonies representing the clients of a biometrics authentication system. The biometric template classification process is modeled as the aggregation of ants to colonies. When test input data is captured -- a new ant in our representation -- it will be influenced by the deposited phermonones related to the population of the colonies. We experiment with the Aggregation Pheromone density based Classifier (APC), and our results show that APC outperforms ``traditional'' techniques -- like 1-nearest-neighbour and Support Vector Machines -- and we also show that performance of APC are comparable to several state of the art face verification algorithms. The results here presented let us conclude that swarm intelligence approaches represent a very promising direction for further investigations for biometrics verification and identification