Towards detection of glasses in facial images

Jiang, Xiaoyi; Binkert, M.; Achermann, B.; Bunke, Horst

doi:10.1109/icpr.1998.711877

Cited by 20 publications

(22 citation statements)

References 20 publications

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“…Detection is carried out using edge information within a small area defined between the eyes. Detection is similar to the method proposed in [1]. As mentioned in [1], the nosepiece is one of the most common features existing on all of the glasses.…”

Section: Glasses Detection Extraction and Removalmentioning

confidence: 90%

“…Detection is similar to the method proposed in [1]. As mentioned in [1], the nosepiece is one of the most common features existing on all of the glasses. Extraction is achieved with a deformable contour, combining edge and geometrical features.…”

Section: Glasses Detection Extraction and Removalmentioning

confidence: 90%

“…In [1], six measures for the likelihood of glasses in different regions for detecting the presence of glasses are introduced. Furthermore, a combination of them turns out to consistently improve the performance of the individual measures.…”

Section: Glasses Detection Extraction and Removalmentioning

confidence: 99%

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Glasses detection on real images based on robust alignment

Fernández

García

Usamentiaga

et al. 2015

Machine Vision and Applications

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Automatic glasses detection on real face images is a challenging problem due to different appearance variations. Nevertheless, glasses detection on face images has not been thoroughly investigated. In this paper, an innovative algorithm for automatic glasses detection based on Robust Local Binary Pattern and robust alignment is proposed. Firstly, images are preprocessed and normalized in order to deal with scale and rotation. Secondly, eye glasses region is detected considering that the nosepiece of the glasses is usually placed at the same level as the center of the eyes in both height and width. Thirdly, Robust Local Binary Pattern is built to describe the eyes region, and finally, support vector machine is used to classify the LBP features. This algorithm can be applied as the first step of a glasses removal algorithm due to its robustness and speed. The proposed algorithm has been tested over the Labeled Faces in the Wild database showing a 98.65 % recognition rate. Influences of the resolution, the alignment of the normalized images and the number of divisions in the LBP operator are also investigated.

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Section: Glasses Detection Extraction and Removalmentioning

confidence: 90%

Section: Glasses Detection Extraction and Removalmentioning

confidence: 90%

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Glasses detection on real images based on robust alignment

Fernández

García

Usamentiaga

et al. 2015

Machine Vision and Applications

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“…Training is performed with a boot-strap learning algorithm [21]. Generating a training set for the SVM is a challenging task because of the difficulty in placing "characteristic" non-face images in the training set.…”

Section: Support Vector Machinesmentioning

confidence: 99%

The Use of Neural Networks in Real-time Face Detection

Curran

Li²,

mccaughley³

2005

J. of Computer Science

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As continual research is being conducted in the area of computer vision, one of the most practical applications under vigorous development is in the construction of a robust real-time face detection system. Successfully constructing a real-time face detection system not only implies a system capable of analyzing video streams, but also naturally leads onto the solution to the problems of extremely constraint testing environments. Analyzing a video sequence is the current challenge since faces are constantly in dynamic motion, presenting many different possible rotational and illumination conditions. While solutions to the task of face detection have been presented, detection performances of many systems are heavily dependent upon a strictly constrained environment. The problem of detecting faces under gross variations remains largely uncovered. This study presents a real-time face detection system which uses an image based neural network to detect images.

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“…Não apenas a identificação biométrica pela íris, como a identificação por impressão digital ou faces, procuram identificar um indivíduo através de informações biométricas pessoais (o que ele é) e não através de informações que o indivíduo sabe (senhas) ou possui (cartões e outros tipos de chaves) (JIANG et al, 2000).…”

Section: Considerações Iniciaisunclassified

Uma nova abordagem para reconhecimento biométrico baseado em características dinâmicas da íris humana

Costa¹

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Uma nova abordagem para reconhecimento biométrico baseado em características dinâmicas da íris humanaA todas as pessoas que me ajudaram de alguma maneira para realização deste trabalho e de maneira injusta não foram aqui citadas peço perdão e ofereço um muito obrigado de todo o meu coração. RESUMOA identificação pessoal através da análise da textura da íris é um método de identificação biométrico de grande eficiência. Algoritmos e técnicas foram desenvolvidos levando-se em consideração as características de textura da imagem da íris do olho humano. No entanto, essas características por serem estáticas são também susceptíveis a fraudes, ou seja, uma foto pode substituir a íris em análise. Por isso, este trabalho propõe um método para extrair as características de textura da íris durante a contração e dilatação da pupila, além das próprias características dinâmicas de contração e dilatação. Para isso foi desenvolvido um novo sistema de aquisição da imagem utilizando iluminação NIR(Near Infra-Red) e levando-se em conta o Reflexo Consensual dos olhos. As características são medidas de acordo com um padrão dinâmico de iluminação controlado pelo programa.Com isso, é possível aumentar a segurança de dispositivos de reconhecimento biométrico de pessoas através da íris, pois, somente íris vivas podem ser utilizadas. Os resultados mostram um índice de precisão significativo na capacidade de discriminação destas características.Palavras-chave: reconhecimento de íris, reflexo consensual, características dinâmicas, biometria.x ABSTRACTThe personal identification by iris texture analysis is a highly effective biometric identification method. Some algorithms and techniques were developed, taking into consideration the texture features of the iris image in the human eye. Nonetheless, such features, due to the fact that they are static, are also susceptible to fraud. That is, a picture can replace the iris in an analysis. For that reason, this work proposes a method for extracting texture features of the iris during the pupil contraction and dilation, in addition to the dynamic contraction and dilation features themselves. Therefore, it was developed a new image acquisition system through NIR (Near Infra-Red) illumination, considering the Consensual Reflex of the eyes. Features are measured according to a dynamic illumination standard controlled by the software and are afterwards selected by means of data mining.Then it is possible to increase the safety in the biometric recognition devices of people through their iris, since only living irises can be utilized. Results show a significant precision index in determining such features.

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Towards detection of glasses in facial images

Cited by 20 publications

References 20 publications

Glasses detection on real images based on robust alignment

Glasses detection on real images based on robust alignment

The Use of Neural Networks in Real-time Face Detection

Uma nova abordagem para reconhecimento biométrico baseado em características dinâmicas da íris humana

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