Feature-based human face detection

Yow, Kin Choong; Cipolla, Roberto

doi:10.1016/s0262-8856(97)00003-6

Cited by 288 publications

(110 citation statements)

References 14 publications

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“…To preserve the face contour the heuristics and edge maps are used by Graf et al to identify facial features and faces in gray scale images [2]. In [3], Yow and Cipolla presented a method that is based on extracting features from the image and collect their contextual evidence.…”

Section: Facial Featuresmentioning

confidence: 99%

A Fusion based Approach of Face Detection using Viola Jones and Skin Color Modeling Technique

Goyal¹,

Dev²

2015

IJCA

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Human computer interaction is dealing with different branches of learning that involves the interaction of human with machines directly. For an effective interaction between the machines and humans a user friendly and interactive interface needed. Automatic Face Detection and Recognition proves as a solution for that. Face is a physiological characteristic that indicates what we have in the mind by analyzing the facial expressions such as screaming, happiness and so on. The various factors such as identity, gender, expression, age and pose can be efficiently analyzed by observing the information contained a face. It is very easy for humans to recognize faces because human beings are very good in recognizing process, but it seems to be challenging for machines in the field of computer vision. Therefore, many researchers contribute their interest and attention in this emerging field. In this paper our focus on analyzing the existing faces detection techniques and combining some compatible techniques to form a model with improved efficiency and better detection rate.

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Section: Facial Featuresmentioning

confidence: 99%

A Fusion based Approach of Face Detection using Viola Jones and Skin Color Modeling Technique

Goyal¹,

Dev²

2015

IJCA

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“…Face Detection: Several approaches to face detection have been described, ranging from using Eigenfaces [18], feature based [21,5] and neural network based methods [13]. The advantages of applying neural networks for the face detection task are quite obvious: The facial image is characterized directly in terms of pixel intensities, and according to the two-class problem at hand (face, no face) a training pattern set can be used to adjust the parameters of the classier.…”

Section: Person Vericationmentioning

confidence: 99%

An approach to multi-modal human–machine interaction for intelligent service robots

Böhme

Wilhelm

Key

et al. 2003

Robotics and Autonomous Systems

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The paper describes a multimodal scheme for humanrobot interaction suited for a wide range of intelligent service robot applications. Operating in unengineered, cluttered, and crowded environments, such robots have to be able to actively contact potential users in their surroundings and to oer their services in an appropriate manner. Starting from a real application scenario, the usage of a robot as mobile information kiosk in a home store, some reliable methods for vision-based interaction, noise analysis and speech output have been developed. These methods are integrated into a prototypical interaction cycle that can be assumed as a general approach to human-machine interaction. Experimental results demonstrate the strengths and weaknesses of the proposed methods.

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“…Um número considerável de métodos usam a informação de cor para detectar faces (DAÍ & NAKANO, 1996) (CAI & GOSHTASBY, 1999) (YACHIDA et al, 1999) (KIM et al, 2000). Existem métodos que utilizam a informação da geometria da face para detectar faces (JENG et al, 1998) (YOW & CIPOLLA, 1997) (LIN & FAN,2001). …”

Section: Alguns Métodos Para Detecção De Faces Humanasunclassified

“…Um método desenvolvido por Yow & Cipolla (1997) usa filtros Gaussianos para localizar características semelhantes a barras horizontais nas imagens. Assim, característi-cas alongadas tal como olhos e boca são localizadas.…”

Section: Geometria Da Faceunclassified

Investigação biométrica em imagens digitais para detecção de faces humanas através da proporção divina

Prado¹

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AgradecimentosAo Prof. Dr. Adilson Gonzaga, mestre e amigo, pelos ensinamentos e orientações precisas e, principalmente, pela confiança e incentivo para a realização desta pesquisa, minha sincera gratidão.Ao Prof. Dr. Maurílio Boaventura por fundamentar os meus primeiros passos na área de pesquisa com muita paciência e dedicação, o que possibilitou a busca de novos caminhos, meu sincero reconhecimento.Aos amigos do Laboratório de Visão de Computacional -LAVI, Anderson e Thiago, pelo aprendizado diário, compartilhar de idéias e bons momentos.À todos os colegas do Departamento de Engenharia Elétrica da EESC-USP, em especial Clayton, Celso França, Fábio e Marcelo pelo companheirismo.Aos professores e funcionários do Departamento de Engenharia Elétrica que sempre estiveram dispostos a colaborar e, às secretárias Denise e Marisa pela atenção e 'ajudas burocráticas'. Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq pelo apoio financeiro concedido durante a realização desta pesquisa, sem o qual a mesma não seria possível.Aos amigos 'mosqueteiros', Cassius e Edward, que com muito bom humor e inteligên-cia estiveram sempre abertos para compartilhar os desafios da vida profissional.Aos amigos da república, Anselmo e Nivaldo, pelas conversas sempre divertidas durante as madrugadas.À família Palma que proporcionou agradáveis momentos de conversas e orientações em meio a passeios e viagens nesses últimos anos. i À minha namorada Ariane, que com carinho dispôs de seu tempo na tarefa paciente de revisar partes importantes deste texto e, especialmente, pela compreensão, companheirismo e amor. À você meus agradecimentos repletos de carinho e amor.À minha família, que de forma sábia, humilde e amorosa ensinam a vencer em cada momento da vida. E neste momento especial, a minha sincera gratidão pelo apoio constante, pelas orações fortalecedoras e por jamais permitirem que eu desistisse dos meus desejos e sonhos profissionais. E, principalmente, ao Senhor Deus, o Autor da Vida, que me revestiu de força, perseverança e sabedoria para desenvolver toda esta pesquisa mostrando mais uma vez que para Ele nada é impossível.ii The increase of recognition systems in the contemporary world has demanded robust and agile detection processes. From teleconference systems to security and monitoring mechanisms, the Detection and Recognition of people have became constantly used and applied. In attempt to search for alternative ways to solve both detection and recognition problems, this work proposes the utilization of biometric measures, taken in digital image of human faces. From the study of such measures, it's possible to verify face proportions, especially the Divine Proportion, which could allows, in the future, to implement the detection and/or recognition algorithms that utilize such proportions. Due to small amount of scientific publications that use the Divine Proportion as a way of detection and/or recognition in image processing, this investigation contributes with some steps in this direction.

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Feature-based human face detection

Cited by 288 publications

References 14 publications

A Fusion based Approach of Face Detection using Viola Jones and Skin Color Modeling Technique

A Fusion based Approach of Face Detection using Viola Jones and Skin Color Modeling Technique

An approach to multi-modal human–machine interaction for intelligent service robots

Investigação biométrica em imagens digitais para detecção de faces humanas através da proporção divina

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