Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition

Tang, Jialin; Su, Qinglang; Su, Binghua; Fong, Simon; Cao, Wei; Gong, Xueyuan

doi:10.1016/j.cmpb.2020.105622

Cited by 49 publications

(24 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Third, in the Human Face recognition problem the GbC performed up to 88.25%. Note that alternative, state-of-the-art classifiers in image pattern recognition have reported classification accuracies ranging from 93% up to 96% by ANFIS [49], as well as from 92% up to 100% by a CNN deep learning scheme [50]. Again, alternative image recognition methods typically used orders of magnitude more data than GbC did.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Satu metode yang paling banyak digunakan untuk mendapatkan informasi tekstur dari citra adalah local binary patterns (LBP) [11]. Penelitian [12] mengombinasikan local binary patterns dan convolutional neural networks (CNN) untuk melakukan pengenalan wajah dan menghasilkan akurasi 100% dan 97.51% pada dua dataset yang digunakan. Penelitian [13] menggunakan local binary patterns pada kasus pengenalan wajah tiga dimensi.…”

Section: Penyebaran Awal Virus Ini Ditemukan DIunclassified

Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns

Wihandika

2021

RESTI

View full text Add to dashboard Cite

The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time. This arises because the spread of the SARS-CoV-2 virus also continued to occur in the community. Of the five points in 5M that has been initiated by the government, the focus of this study is the use of face masks. In this study, an image-based automatic mask detection method using a classification approach is proposed. This method can be used in automated systems to increase public discipline in wearing masks to suppress the spread of the SARS-CoV-2 virus. The classes used in the classification are "with mask" and "without mask". The adjacent evaluation local binary patterns (AELBP) method, which is an extension of the local binary patterns (LBP) method, is used to extract the texture features of each image. Tests were carried out on 2,172 facial images of various sizes, facial accessories, and facial expressions. The test results using the AELBP method show that the accuracy and F-measure are 98.39% and 98.08%, respectively. This result is better than other methods which are also evaluated. In addition, testing of the AELBP method execution time shows that this method is feasible to use on real systems.

show abstract

“…In their experiments, their approach was found to enhance tolerance to posture, expression, and illumination, and improve the accuracy of face recognition. However, their approach was found to have poor model generalization performance, which is normally caused by the learning algorithm being trapped in a local minimum [17]. Zhou, Constantinides, Huang, and Zhang proposed an improved center symmetric LBP for face recognition, with the experimental results on some face datasets indicating that a higher RR could be obtained by employing the proposed method with nearest neighbor classification [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Isnanto

Rochim

Eridani

et al. 2021

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

show abstract

Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition

Cited by 49 publications

References 2 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

Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Contact Info

Product

Resources

About