Normalization of face illumination using basic knowledge and information extracted from a single image

Smiatacz, Maciej

doi:10.1016/j.ins.2018.08.034

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…It is obviously unreasonable: In the first example (1), the center pixel and its neighborhood pixels are undoubtedly considered different. However, in the second example (2), no matter the center pixel or its neighborhood pixels, they are very similar. Thus, it is unreasonable to set the threshold t to a fixed value, which should vary with the gray value of the central pixel in local window.…”

Section: A: Adaptive Symmetrical Ternary Pattern (Astp)mentioning

confidence: 93%

“…In recent years, with the development of artificial intelligence technology, image recognition has become a hot topic in computer vision, which is widely applied in the fields of biometric recognition, object detection, image retrieval, license plate recognition, and etc.. However, complex illumination conditions including insufficient illumination, uneven illumination, excessive illumination and illumination variation bring great challenge in image recognition [1], [2]. Illumination variation has dramatic effect on many traditional image recognition methods compared with other influence factors.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Illumination-Insensitive Feature Extraction Method

Gao

Zhang

et al. 2020

IEEE Access

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Image description is a great challenge in the field of computer vision under complex illumination condition. The complex illumination condition is usually unavoidable and unpredictable in real application. In this paper, we propose a novel generalized image descriptor, named as Anisotropy Weber Adapted Symmetric Ternary Pattern (AWASTP), which can overcome the directional inseparability of Weber Local Descriptor (WLD) and invariant threshold of Local Ternary Pattern (LTP). More particularly, we heighten the discriminative effectiveness of image description under complex illumination condition in several ways to restrain the effect of illumination variation. Firstly, a novel selection scheme for scale and angle is proposed. Based on this, an improved anisotropic Laplacian of Gaussian(ALOG) operator model is established by introducing the scale and angle parameter, moreover, an Anisotropic Weber Local Descriptor (AWLD) is presented, which can achieve more rich detailed information of illumination-insensitive feature. Secondly, an Adaptive Symmetric Ternary Pattern (ASTP) algorithm is proposed based on Weber criterion to generate more accurate threshold judgment according to the region characteristics. Thirdly, a twodimensional AWASTP histogram is created to enhance the discriminative power and represent illuminationinsensitive feature description. We conduct many experiments on benchmark databases, such as CMUPIE, FERET, PhoTex, RawFoot, and etc. Experimental results demonstrate the effectiveness of the proposed approach under different illumination conditions.

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Section: A: Adaptive Symmetrical Ternary Pattern (Astp)mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A Novel Illumination-Insensitive Feature Extraction Method

Gao

Zhang

et al. 2020

IEEE Access

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“…Generally, the face recognition process uses two stages, namely the training stage and the testing phase. However, there were approaches that do not use the training process, for example the SNE method [11]. The method used in this research is the Robust Regression, which uses training process and testing process.…”

Section: A the Face Recognition Methodsmentioning

confidence: 99%

The Robust Regression Performance for Face Recognition with Lighting Condition Variation of Training Data

Nugroho¹,

Anny²

2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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In this research, the Robust Regression method used for face recognition tested its performance with illumination variations on the training dataset. Experiments were carried out using Cropped Yale Face Database B. By using this standard face database, generally the data for the training process used all images in subset 1 and the testing process was carried out on all images in other subsets. The training process in this method is done to create a regressor or predictor. In this research experiment, training data use each subset. Also, this research experiment will also combine several images from all subsets. The experimental results show that the use of subset 1 images as training data turns out to produce the lowest facial recognition performance where the accuracy is 90.00%. The use of other subsets as training datasets can deliver better facial recognition performance. The highest facial recognition performance is achieved through the use of combined images of sample images from all subsets, where accuracy reaches 99.81%.

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“…RDF and RDF Schema (collectively known as RDF(S)) are the core of the Semantic Web. Nowadays, RDF(S) have been increasingly applied in a wide range of Web-based application scenarios, such as semantic data integration (Arsic et al, 2019), semantic search (Xiong, Power and Callan, 2017;Zheng et al, 2019), semantic analysis of Big Data (Smiatacz, 2018;Shen, Hu and Tzeng, 2017), decision making (Rubio-Largo et al, 2017;Zhou et al, 2017) and so on. Currently, RDF(S) has become the de-facto standard of representing and handling data semantics.…”

Section: Introductionmentioning

confidence: 99%

RDF(S) Store in Object-Relational Databases

Ma,

Li,

et al. 2023

Journal of Database Management

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The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

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Normalization of face illumination using basic knowledge and information extracted from a single image

Cited by 9 publications

References 22 publications

A Novel Illumination-Insensitive Feature Extraction Method

A Novel Illumination-Insensitive Feature Extraction Method

The Robust Regression Performance for Face Recognition with Lighting Condition Variation of Training Data

RDF(S) Store in Object-Relational Databases

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