scholarly journals Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

2019 ◽  
Vol 9 (13) ◽  
pp. 2709 ◽  
Author(s):  
Leslie Ching Ow Tiong ◽  
Yunli Lee ◽  
Andrew Beng Jin Teoh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Performance ◽  
Texture Descriptor ◽  
Late Fusion ◽  
In The Wild ◽  
Periocular Recognition ◽  
Rgb Image

Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. The proposed network aggregates the RGB image and the OCLBCP descriptor by using two distinct late-fusion layers. We demonstrate that the proposed network benefits from the RGB image and thee OCLBCP descriptor can gain better recognition performance. A new database, namely an Ethnic-ocular database of periocular in the wild, is introduced and shared for benchmarking. In addition, three publicly accessible databases, namely AR, CASIA-iris distance and UBIPr, have been used to evaluate the proposed network. When compared against several competing networks on these databases, the proposed network achieved better performances in both recognition and verification tasks.

scholarly journals MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network

2020 ◽  
Vol 19 (5) ◽  
pp. 1292-1300
Author(s):  
Xi QIAO ◽  
Yan-zhou LI ◽  
Guang-yuan SU ◽  
Hong-kun TIAN ◽  
Shuo ZHANG ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Mikania Micrantha ◽  
In The Wild

scholarly journals REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

SINERGI ◽  
2019 ◽  
Vol 23 (3) ◽  
pp. 239
Author(s):  
Dwi Lydia Zuharah Astuti ◽  
Samsuryadi Samsuryadi ◽  
Dian Palupi Rini
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Customer Service ◽  
Facial Expressions ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
In The Wild

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases.  This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

scholarly journals Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jemin Lee ◽  
Jinse Kwon ◽  
Hyungshin Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Management System ◽  
Network Models ◽  
Neural Network Models ◽  
In The Wild

Smartwatches provide a useful feature whereby users can be directly aware of incoming notifications by vibration. However, such prompt awareness causes high distractions to users. To remedy the distraction problem, we propose an intelligent notification management for smartwatch users. The goal of our management system is not only to reduce the annoying notifications but also to provide the important notifications that users will swiftly react to. To analyze how to respond to the notifications daily, we have collected 20,353 in-the-wild notifications. Subsequently, we trained the convolutional neural network models to classify important notifications according to the users’ contexts. Finally, the proposed management allows important notifications to be forwarded to a smartwatch. As experiment results show, the proposed method can reduce the number of unwanted notifications on smartwatches by up to 81%.

scholarly journals Convolutional Neural Network-Based Periocular Recognition in Surveillance Environments

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57291-57310 ◽  
Author(s):  
Min Cheol Kim ◽  
Ja Hyung Koo ◽  
Se Woon Cho ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Periocular Recognition

scholarly journals Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 39
Author(s):  
Hongpeng Liao ◽  
Jianwu Xu ◽  
Zhuliang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Experimental Results ◽  
Redundant Information ◽  
Promising Tool ◽  
Information Bottleneck

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

scholarly journals Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

2019 ◽  
Vol 14 (1) ◽  
pp. 333 ◽  
Author(s):  
Nik Noor Akmal Abdul Hamid ◽  
Rabiatul Adawiya Razali ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Operation Time ◽  
Machine Learning Techniques ◽  
Bag Of Features ◽  
Learning Techniques ◽  
Recognition Result ◽  
Different Types

This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition.  Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost.  On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset.  Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition.   The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.

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