Person Location Service on the Planetary Sensor Network

This paper gives a prototype application which can provide a person location service on the IrisNet. Two crucial technologies face detection and face recognition underpinning such image and video data mining service are explained. For the face detection, authors use 4 types of simple rectangles as features, Adaboost as the learning algorithm to select the important features for classification, and finally generate a cascade of classifiers which is extremely fast on the face detection task. As for the face recognition, the authors develop Adaptive Principle Components Analysis (APCA) to improve the robustness of principal Components Analysis (PCA) to nuisance factors such as lighting and expression. APCA also can recognize faces from single face which is suitable in a data mining situatio

Authentication Based on Periocular Biometrics and Skin Tone

Face images with masks have a major effect on the identification and authentication of people with masks covering key facial features such as noses and mouths. In this paper, we propose to use periocular region and skin tone for authenticating users with masked faces. We first extract the periocular region of faces with masks, then detect the skin tone for each face. We then train models using machine learning algorithms Random Forest, XGBoost, and Decision Trees using skin tone information and perform classification on two datasets. Experiment results show these models had good performance

Reconhecimento de faces humanas através de técnicas de inteligência artificial aplicadas a formas 3D

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Esta tese propõe uma nova metodologia de reconhecimento de faces humanas. Diferente dos tradicionais métodos que empregam imagens bidimensionais e níveis de cinza, o paradigma aqui proposto utiliza a forma geométrica da face como parâmetro de avaliação e autenticação. Destacam-se como vantagens desta técnica o melhor desempenho principalmente frente aos problemas de iluminação e posicionamento espacial. Este trabalho engloba duas áreas de conhecimento distintas. A primeira, abordando aspectos da extração da forma tridimensional da face humana e a segunda o reconhecimento facial. A metodologia de extração da geometria baseia-se no método de Perfilometria de Fourier para obter a forma geométrica das faces. A metodologia de reconhecimento de faces divide-se em duas abordagens distintas: A primeira delas do tipo algorítmica, por Raciocínio Baseado em Casos - RBC empregando da distância de Hamming como medidor da similitude entre duas formas de faces e a segunda do tipo conexionista, baseando-se no emprego de Redes Neurais Artificiais #RNA do tipo Funções de Base Radiais - FBR, para a classificação das faces. O modelo de reconhecimento adotado nesta tese é o da verificação, onde o indivíduo se apresenta previamente e ao sistema é atribuída à tarefa de verificação da veracidade da identidade alegada. A inspiração biológica está totalmente presente neste trabalho, em primeiro lugar porque os seres humanos, no processo de reconhecimento de seus semelhantes, utilizam se das faces humanas e a forma da face é uma das grandezas identificadoras.Em segundo lugar porque as metodologias de reconhecimento inspiradas em Redes Neurais Artificiais são inerentemente paradigmas biológicos. Finalmente empregando uma grande base de faces humanas tridimensionais, são mostrados os resultados da aplicação dos paradigmas de verificação desenvolvidos, comparando-se os resultados obtidos através de uma análise detalhada

Detection of Human Faces Using Decision Trees

This paper proposes a novel algorithm for face detection using decision trees (DT) and shows its generality and feasibility using a data base consisting of 2,340 face images from the FERET data base (corresponding to 817 subjects and including 190 sets of duplicates) over a semi uniform background. The approach used for face detection involves three main stages, those of location, cropping, and post processing. The first stage finds a rough approximation for the possible location of the face box, the second stage will refine it, and the last stage decides whether a face is present in the image and if the answer is positive would normalize the face image. The algorithm does not require multiple (scale) templates and the accuracy achieved is 96%. Accuracy is based on the visual observation that the face box includes both eyes, nose, and mouth, and that the top side of the box is below the hairline. Experiments were also performed to assess the accuracy of the algorithm in rejecting image..

Summary of ”Detection of Human Faces Using Decision Trees”

Wechsler (1996) propose a novel algorithm for face detection using decision trees (DT), and demonstrating its feasibility and accuracy by carrying out the experiments on data from FERET facial database. The importance of their work is explained by the fact that most face recognition algorithms that are used in numerous security applications expect a processed face image as an input, thus omitting the face detection stage. Among the proposed algorithms for face detection the highest accuracy achieved is 92.9%, and testing is performed on relatively small image databases (e.g. 65 images). The goal of this work is to improve the accuracy of face detection and perform tests on larger data sets. Face detection is the first stage of face recognition, followed by image normalization, face identification, and post-processing stages. It enables face recognition applications to concentrate their resources on the face area by supplying them with a face ’box ’ boundary (a face image segmented from it’s background). The authors introduced the following three stages of their face detection algorithm: location- finding a possible location of the face box, cropping- refinement of the outpu