Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources

Ahmed, Nehal Khaled; Hemayed, Elsayed E.; Fayek, Magda B.

doi:10.3390/bdcc4030019

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…Our results are higher (+0.95%) than DeepFace [32], higher(+0.70%) than DeepFace-Siamese [32]. However, the accuracy of SDBCN is 2% lower than that of PSI [34], HSN [35]. Since SDBCN is dedicated to cow face recognition rather than human face recognition such as LFW dataset, this has been able to demonstrate its performance.…”

Section: Modelsmentioning

confidence: 63%

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Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Gao

Pan

2022

IEEE Access

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Dairy cow face recognition using Neural Networks has several hurdles. For example, there are only a few instances of each individual. The positions and angles of the individuals in the image fluctuate considerably, the differences between individuals are not apparent, and the number of individuals that the network has not been trained on is enormous, etc. In this paper, an enhanced Siamese Neural Network is used to overcome these barriers. First, a combination of Dense Block and Capsule Network is employed as a feature extractor to keep the spatial information of features while expanding the feature extraction capabilities of the Convolutional Neural Network. Second, image pairings are processed through the Siamese Neural Network to obtain bivariate features. Finally, image recognition is achieved via the correlation analysis of bivariate features. We conduct comparison experiments with different networks on a small cow face dataset. The experimental results demonstrate that Siamese DB Capsule Network can learn abstract knowledge about distinct individuals and can be extended to unfamiliar cows for zero-shot learning.

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Section: Modelsmentioning

confidence: 63%

“…DeepFace [32] 95.92 DeepFace-Siamese [32] 96.17 3DMM [33] 92.35 PSI [34] 98.87 HSN [35] 98.95 SDBCN(Proposed Network) 96.87…”

Section: Modelsmentioning

confidence: 99%

Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Gao

Pan

2022

IEEE Access

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“…The model can identify students at-risk of dropping out of school and isolate the causative of this challenge [ 13 ]. A novel hybrid DL model capable of detecting features supportive of face recognition has been proposed to apply the trained model to build a face clustering system based on density-based spatial clustering of applications with noise (DBSCAN) [ 14 ]. Similarly, generative adversarial networks (GANs), a composition of DL models adversarial positioned for generative purposes, have been investigated for kinship face synthesis [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Ezugwu

Oyelade

Ikotun

et al. 2023

Arch Computat Methods Eng

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The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa’s most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

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“…Nowadays, the Siamese neural network has become a popular method in image recognition, partly due to the advantage that it does not require a large amount of data in the inference phase [14][15][16][17][18]. The Siamese neural network takes two samples as the input and outputs the spatial features after dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Sun

et al. 2021

Electronics

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Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

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Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources

Cited by 10 publications

References 32 publications

Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

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