Linguistic Descriptors in Face Recognition

Karczmarek, Paweł; Kiersztyn, Adam; Pedrycz, Witold; Dolecki, Michał

doi:10.1007/s40815-018-0517-0

Cited by 7 publications

(4 citation statements)

References 40 publications

(43 reference statements)

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“…The latter are unique capacities of a GbC scheme that no deep learning scheme possesses. In addition, compared to alternative fuzzy systems for face recognition [26,27], the GbC can operate on structured (tree) data representations of a human face instead of operating solely on vectors of features. Furthermore, by its parameters, the GbC can carry out tunable generalization.…”

Section: Discussionmentioning

confidence: 99%

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Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

et al. 2021

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Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

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Section: Discussionmentioning

confidence: 99%

“…In other words, a graph in previous LC works has been used only once, for data preprocessing. Similarly, different authors have recently employed an interesting hierarchic and/or a linguistic descriptor approach for extracting vectors of features regarding face recognition problems [26,27].…”

Section: Introductionmentioning

confidence: 99%

Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

et al. 2021

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“…In healthcare, body mass index (Ajala et al, 2017 ), calculated with weight and height, is used in applications where patients’ health is affected by overweight conditions. In facial recognition, region features (Karczmarek et al, 2018 ), such as face oval, upper lip, lower lip, eyebrow, eye, cheek, nose bridge, and nose bottom, are used for better identification. Besides the above feature selection, different industry sectors might favor different analytic methods due to different limitations.…”

Section: Current Interdisciplinary Research Between Data Analytics and Industry 40mentioning

confidence: 99%

Data Analytics in Industry 4.0: A Survey

Duan

2021

Inf Syst Front

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Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

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“…Movement of fireflies in a hyperdimensional input space is controlled by tuning the parameter gamma of the firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time, and recognition accuracy. Karczmarek et al [15] proposed a linguistic descriptor-based approach to the problem of face identification realized by both humans and computers. is approach is motivated by an evident observation that linguistic descriptors offer an ability to formalize and exploit important pieces of knowledge describing human's face.…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

Talab

Awang

Ansari³

2020

International Journal of Optics

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Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.

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Linguistic Descriptors in Face Recognition

Cited by 7 publications

References 40 publications

Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Data Analytics in Industry 4.0: A Survey

A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

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