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Author

Siming Zheng

Bio: Siming Zheng is an academic researcher. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 2, co-authored 3 publications receiving 8 citations.

Papers
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Journal ArticleDOI
02 Dec 2019-PeerJ
TL;DR: In this experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks, which costs less than traditional methods.
Abstract: As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

6 citations

Posted Content
TL;DR: A fine-tuning neural network model based on the Mask R-CNN and Inception V4 neural networkmodel, which integrates every component in an overall system that combines the iris detection, extraction, and recognition function as an iris recognition system.
Abstract: In recent years, mobile Internet has accelerated the proliferation of smart mobile development. The mobile payment, mobile security and privacy protection have become the focus of widespread attention. Iris recognition becomes a high-security authentication technology in these fields, it is widely used in distinct science fields in biometric authentication fields. The Convolutional Neural Network (CNN) is one of the mainstream deep learning approaches for image recognition, whereas its anti-noise ability is weak and needs a certain amount of memory to train in image classification tasks. Under these conditions we put forward a fine-tuning neural network model based on the Mask R-CNN and Inception V4 neural network model, which integrates every component in an overall system that combines the iris detection, extraction, and recognition function as an iris recognition system. The proposed framework has the characteristics of scalability and high availability; it not only can learn part-whole relationships of the iris image but also enhancing the robustness of the whole framework. Importantly, the proposed model can be trained using the different spectrum of samples, such as Visible Wavelength (VW) and Near Infrared (NIR) iris biometric databases. The recognition average accuracy of 99.10% is achieved while executing in the mobile edge calculation device of the Jetson Nano.

3 citations

Posted Content
TL;DR: A fine-tuning neural network model based on the Mask R-CNN and Inception V4 neural networkmodel, which integrates every component in an overall system that combines the iris detection, extraction, and recognition function as an iris recognition system.
Abstract: In recent years, mobile Internet has accelerated the proliferation of smart mobile development. The mobile payment, mobile security and privacy protection have become the focus of widespread attention. Iris recognition becomes a high-security authentication technology in these fields, it is widely used in distinct science fields in biometric authentication fields. The Convolutional Neural Network (CNN) is one of the mainstream deep learning approaches for image recognition, whereas its anti-noise ability is weak and needs a certain amount of memory to train in image classification tasks. Under these conditions we put forward a fine-tuning neural network model based on the Mask R-CNN and Inception V4 neural network model, which integrates every component in an overall system that combines the iris detection, extraction, and recognition function as an iris recognition system. The proposed framework has the characteristics of scalability and high availability; it not only can learn part-whole relationships of the iris image but also enhancing the robustness of the whole framework. Importantly, the proposed model can be trained using the different spectrum of samples, such as Visible Wavelength (VW) and Near Infrared (NIR) iris biometric databases. The recognition average accuracy of 99.10% is achieved while executing in the mobile edge calculation device of the Jetson Nano.

1 citations


Cited by
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Journal ArticleDOI
01 Jan 2021-PeerJ
TL;DR: Zhang et al. as discussed by the authors employed a unified attention and context mapping (ACM) block within the convolutional layers of network, without any additional computational resources overhead to learn the attention with reference to their spatial locations in context of the whole image.
Abstract: The discriminative parts of people's appearance play a significant role in their re-identification across non overlapping camera views. However, just focusing on the discriminative or attention regions without catering the contextual information does not always help. It is more important to learn the attention with reference to their spatial locations in context of the whole image. Current person re-identification (re-id) approaches either use separate modules or classifiers to learn both of these; the attention and its context, resulting in highly expensive person re-id solutions. In this work, instead of handling attentions and the context separately, we employ a unified attention and context mapping (ACM) block within the convolutional layers of network, without any additional computational resources overhead. The ACM block captures the attention regions as well as the relevant contextual information in a stochastic manner and enriches the final person representations for robust person re-identification. We evaluate the proposed method on 04 public benchmarks of person re-identification i.e., Market1501, DukeMTMC-Reid, CUHK03 and MSMT17 and find that the ACM block consistently improves the performance of person re-identification over the baseline networks.

8 citations

Journal Article
TL;DR: This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems and its increasing method of continually studying and adapting to the person's changes.
Abstract: Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control.

5 citations

Book ChapterDOI
07 Oct 2020
TL;DR: In this article, 3D face recognition was performed with different facial expressions and occlusions based on the data of 105 people using Bosphorus database and 3D point clouds.
Abstract: With developing technology and urbanization, smart city applications have increased. Accordingly, this development brought some difficulties such as public security risk. Identifying people’s identities is a requirement in both smart city challenges and smart environment or smart interaction difficulties. Face recognition has a huge potential for people’s identification. It was possible to perform face recognition applications in larger databases and different situations with the development of deep learning methods. 2D images are usually used for face recognition applications. However, different challenges such as pose change and illumination cause difficulties in 2D facial recognition applications. Laser scanning technology has provided the production of 3D point clouds, including the geometric information of the faces. When the point clouds are combined with deep learning techniques, 3D face recognition has great potential. In the study, 2D images were created for facial recognition using feature maps obtained from 3D point clouds. ResNet-18, ResNet-50 and ResNet-101 architectures, which are different versions of ResNet architecture, were used for classification purposes. Bosphorus database was used in the study. 3D Face recognition was performed with different facial expressions and occlusions based on the data of 105 people. As a result of the study, overall accuracy was obtained with ResNet-18, ResNet-50, and ResNet-101 architectures at 77.36%, 77.03% and 81.54% respectively.

5 citations