Gender and texture classification: A comparative analysis using 13 variants of local binary patterns

Hadid, Abdenour; Ylioinas, Juha; Bengherabi, Messaoud; Ghahramani, Mohammad; Taleb‐Ahmed, Abdelmalik

doi:10.1016/j.patrec.2015.04.017

Cited by 36 publications

(18 citation statements)

References 23 publications

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“…In literature, many gender classification methods are applied only to frontal or near-frontal face images [37,38,7,39,40]. For the sake of fair comparison, we report only results of methods using unconstrained types of images [16,9,41,42,43,8,18,12] and we omit results of methods using only frontal or near-frontal face images. From the work by Moeini and Mozaffari [16], we report the results of two methods: dictionary learning and separate dictionary learning for gender classification, denoted as DL-GC and SDL-GC, respectively.…”

Section: Gender Classification Accuracymentioning

confidence: 99%

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Afifi

Abdelhamed

2019

Journal of Visual Communication and Image Representation

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Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition.Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.

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Section: Gender Classification Accuracymentioning

confidence: 99%

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Afifi

Abdelhamed

2019

Journal of Visual Communication and Image Representation

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“…There are several modifications of the LBP operator [69,70]. Most of them try to improve the performance of the LBP in specific applications (e.g., texture classification, face recognition, object detection, etc.).…”

Section: Texture Descriptorsmentioning

confidence: 99%

“…However, few works have investigated the performance of the LBP (and its variants) in specific applications. This paper is inspired by the work of Hadid et al [69], who compared the performance of 13 different LBP-based methods in gender recognition applications. Our focus is to test the performance of LBP-based descriptors in IQA applications.…”

Section: Texture Descriptorsmentioning

confidence: 99%

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On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.The filters W i are learned via independent component analysis (ICA). The binarized features b i are aggregated following the same procedure described for generating the LBP labels. The descriptive features are obtained by computing the histogram of the aggregated data.Similarly to the LBP, which generates LBP channels, the BSIF generates coded images. These coded images are the set of labels generated after the binarized features are computed using Equation (17) and aggregated using Equation (1). The aggregation of BSIF results is based on a selected number of bits, instead of the number of neighbors of the labeled pixel. The labeling depends on the relationship between the patch size l and the number of binarized features b i . Figure 6 shows the BSIF coded images corresponding to the same reference using different BSIF parameters. As can be seen in this figure, the textured information depends on the patch size l and on the number of bits. The number of bits is less or equal l 2 − 1. This is the reason why the second column does not contain BSIF coded images for 9, 10, 11, or 12 bits. Figure 6 shows that the choice of the number of bits and patch sizes is important for texture analysis algorithms. Therefore, multiscale approaches that incorporate several combinations of these parameters are interesting [74][75][76][77].

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“…Xiaoyang et al (TAN;TRIGGS, 2010) extended the idea of LBP to local ternary pattern (LTP), which considers the magnitude of pixel derivatives along with its sign to generate the ternary code. In the work of Hadid et al (HADID et al, 2015), the authors present a comparative study using 13 variants of local binary patterns for gender classification.…”

Section: List Of Figuresmentioning

confidence: 99%

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

Machado¹

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" Happiness is not something you postpone for the future, it is something you design for the present; if it does not knock, build a door." A textura é um dos principais atributos visuais para a descrição de padrões encontrados na natureza. Diversosmétodos de análise de textura têm sido usadoscomo uma poderosa ferramenta para aplicações reais que envolvem análise de imagens e visão computacional. Entretanto, os métodos existentes não conseguem discriminar com sucesso a complexidade dos padrões de textura. Tais métodos desconsideram a possibilidade de se descrever estruturas de imagens por meio de medidas como a dimensão fractal. Medidas baseadas em fractalidade permitem uma interpretação geométrica não-inteira que possui aplicações encontradas em áreas como matemática, física, e biologia. Sobre esta lacuna metodológica, a hipótese central desta tese é que texturas presentes na natureza podem ser medidas como superfícies fractais irregulares devido à sua geometria complexa, o que pode ser explorado para fins de análise de imagens e visão computacional. Para superar tais limitações, avançando o estado da arte, esta tese se inicia com uma análise das características de texturas baseada em caminhadas aleatórias de agentes sobre superfícies de imagens. Esta primeira análise leva a um método que combina dimensão fractal com caminhadas de agentes sobre a superfície de imagens. Em uma segunda abordagem, usa-se a difusão não-linear para representar imagens de texturas em diferentes escalas, as quais são descritas via dimensão fractal para fins de classificação de imagens. Em uma terceira proposta, emprega-se a dimensão fractal sobre múltiplas escalas derivadas de uma mesma imagem com o propósito de se realizar a descrição multi-escala de texturas. Um dos propósitos específicos foi a detecção automática de doenças em folhas de soja. Por último, as características de textura foram exploradas segundo uma metodologia baseada em redes complexas para análise de aglomeração de partículas em imagens de nanotecnologia. Os resultados alcançados nesta tese demonstraram o potencial do uso de características de textura. Para tanto foram usadas técnicasde dimensão fractal de Bouligand-Minkowski, multiagentesArtificial Crawlerse difusão não-linear de Perona-Malik, os quais alcançaram eficácia e eficiência comparáveis ao do estado da arte. As contribuições obtidas devem suportar avanços significativos nas áreas de engenharia de materiais, visão computacional, e agricultura.Palavr as-chave: análise de textura, dimensão fractal, sistemas multi-agentes, difusão anisotró-pica, redes complexas. Texture is one of the primary visual attributes used to describe patterns found in nature. Several texture analysis methods have been used as powerful tools for real applications involving analysis and computer vision. However, existing methods do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing image structures by means of measures such as the fractal dimension. Fractality-based mea...

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Gender and texture classification: A comparative analysis using 13 variants of local binary patterns

Cited by 36 publications

References 23 publications

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

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