Feature-based affine-invariant detection and localization of faces

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Feature-Based Affine-Invariant Detection and Localization of Faces

The accuracy of human face detection and localization in images and video is a crucial factor influencing the performance of biometric face authentication and recognition systems. Recently this subject attracted a lot of attention by researchers and companies and its applications emerged in various areas including surveillance, security, and computer games. This thesis describes a novel person-independent method for finding and localizing faces in authentication scenarios. Such scenarios involve situations where a person stands or sits in front of a camera in order to gain access. The objective was to develop an algorithm which uses only still grey-level images, copes well in the presence of cluttered background and accurately localizes faces including eye centres. Many of the methods that have been reported in the literature only partially fulfil these requirements, in particular, a few methods focus on precise eye localization. To address these issues, we propose a novel bottom-up face detection and localization algorithm which exploits statistical feature detectors as the means of image capture effects removal. Our method uses both, a constellation (shape) model and shape-free texture model to select the best face location hypothesis among multiple hypotheses generated by the feature detectors. The constellation model utilizes a distribution of the transformation from a proposed model space into the image space. The texture (appearance) model is based on a cascaded Support Vector Machine classification. Both, an extensive analysis and a performance evaluation on several realistic face databases will be discussed in this thesis. We show that by utilizing the proposed verification of hypotheses, a significant performance boost is achieved compared to the performance of feature detectors alone

Feature-Based Affine-Invariant Detection and Localization of Faces.

The accuracy of human face detection and localization in images and video is a crucial factor influencing the performance of biometric face authentication and recognition systems. Recently this subject attracted a lot of attention by researchers and companies and its applications emerged in various areas including surveillance, security, and computer games. This thesis describes a novel person-independent method for finding and localizing faces in authentication scenarios. Such scenarios involve situations where a person stands or sits in front of a camera in order to gain access. The objective was to develop an algorithm which uses only still grey-level images, copes well in the presence of cluttered background and accurately localizes faces including eye centres. Many of the methods that have been reported in the literature only partially fulfil these requirements, in particular, a few methods focus on precise eye localization. To address these issues, we propose a novel bottom-up face detection and localization algorithm which exploits statistical feature detectors as the means of image capture effects removal. Our method uses both, a constellation (shape) model and shape-free texture model to select the best face location hypothesis among multiple hypotheses generated by the feature detectors. The constellation model utilizes a distribution of the transformation from a proposed model space into the image space. The texture (appearance) model is based on a cascaded Support Vector Machine classification. Both, an extensive analysis and a performance evaluation on several realistic face databases will be discussed in this thesis. We show that by utilizing the proposed verification of hypotheses, a significant performance boost is achieved compared to the performance of feature detectors alone

Expert system for decision making about using of fungicides and zoocides in permanent cultures and vegetables and using of herbicides in field crops:certified methodology

Basic principles of expert system for decision making about using of fungicides and zoocides in permanent cultures and vegetables and using of herbicides in field crops including vegetable are described in methodology. Procedure for decision making about using of pesticides is based on using of multidimensional economic thresholds, mainly for pest and weeds, and on analysis of economic parameters and evaluation of negative impact of pesticides on the environment. Methods of development of damage curves and the model for determination of multidimensional economic threshold for pests and weeds are described. Methods for determination of environmental impact for active substances of herbicides, zoocides and fungicides are described. Principles of model of degradation of residues of pesticides in fruit and vegetable are described and verified on case studies of herbicides in vegetable and zoocides and fungicides in fruit and vegetable

The BANCA database and evaluation protocol, in

Abstract. In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community throug