Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.

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Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification

Entropy ◽

10.3390/e21050456 ◽

2019 ◽

Vol 21 (5) ◽

pp. 456 ◽

Cited By ~ 3

Author(s):

Hao Cheng ◽

Dongze Lian ◽

Shenghua Gao ◽

Yanlin Geng

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Model Selection ◽

Image Classification ◽

Transfer Learning ◽

Deep Neural Networks ◽

Classification Problem ◽

Distribution Model ◽

Classification Problems ◽

Information Bottleneck

Inspired by the pioneering work of the information bottleneck (IB) principle for Deep Neural Networks’ (DNNs) analysis, we thoroughly study the relationship among the model accuracy, I ( X ; T ) and I ( T ; Y ) , where I ( X ; T ) and I ( T ; Y ) are the mutual information of DNN’s output T with input X and label Y. Then, we design an information plane-based framework to evaluate the capability of DNNs (including CNNs) for image classification. Instead of each hidden layer’s output, our framework focuses on the model output T. We successfully apply our framework to many application scenarios arising in deep learning and image classification problems, such as image classification with unbalanced data distribution, model selection, and transfer learning. The experimental results verify the effectiveness of the information plane-based framework: Our framework may facilitate a quick model selection and determine the number of samples needed for each class in the unbalanced classification problem. Furthermore, the framework explains the efficiency of transfer learning in the deep learning area.

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Transfer learning-based Plant Disease Detection

International Journal For Innovative Engineering and Management Research ◽

10.48047/ijiemr/v10/i03/99 ◽

2021 ◽

pp. 469-477

Keyword(s):

Neural Networks ◽

Image Classification ◽

Transfer Learning ◽

Plant Disease ◽

Deep Neural Networks ◽

Main Tool ◽

Classification Problem ◽

Disease Detection ◽

Classification Problems ◽

Crop Species

Deep Neural Networks in the field of Machine Learning (ML) are broadly used for deep learning. Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification problems. Deep neural networks have been highly successful in image classification problems. In this paper, we have shown the use of deep neural networks for plant disease detection, through image classification. This study provides a transfer learning-based solution for detecting multiple diseases in several plant varieties using simple leaf images of healthy and diseased plants taken from PlantVillage dataset. We have addressed a multi-class classification problem in which the models were trained, validated and tested using 11,333 images from 10 different classes containing 2 crop species and 8 diseases. Six different CNN architectures VGG16, InceptionV3, Xception, Resnet50, MobileNet, and DenseNet121 are compared. We found that DenseNet121 achieves best accuracy of 95.48 on test data.

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Comparative Analysis of Deep Neural Networks Architectures for Visual Recognition in the Autonomous Transport Systems

Journal of Physics Conference Series ◽

10.1088/1742-6596/2096/1/012101 ◽

2021 ◽

Vol 2096 (1) ◽

pp. 012101

Author(s):

Y S Ivanov ◽

S V Zhiganov ◽

N N Liubushkina

Keyword(s):

Neural Networks ◽

Visual Recognition ◽

Deep Neural Networks ◽

Classification Problem ◽

Transport Systems ◽

Classification Problems ◽

Comparison Results ◽

Computer Vision Systems ◽

Marine Vessels ◽

Use Of Models

Abstract This paper analyses and presents an experimental investigation of the efficiency of modern models for object recognition in computer vision systems of robotic complexes. In this article, the applicability of transformers for experimental classification problems has been investigated. The comparison results are presented taking into account various limitations specific to robotics. Based on the results of the undertaken studies, recommendations on the use of models in the marine vessels classification problem are proposed

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A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

Applied Sciences ◽

10.3390/app9183876 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3876

Author(s):

Xianghong Lin ◽

Jianyang Zheng

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Building Blocks ◽

Classification Problem ◽

Neuronal Morphology ◽

Batch Size ◽

Learning Approaches ◽

Learning Networks ◽

Classification Problems ◽

Classification Approach

Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems.

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Video Stream Gender Classification Using Shallow CNN

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421550016 ◽

2020 ◽

pp. 2155001

Author(s):

Oleksii Gorokhovatskyi ◽

Olena Peredrii

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Classification Problem ◽

Video Stream ◽

Gender Classification ◽

Single Frame ◽

Static Image ◽

Image Datasets

This paper describes the investigation results about the usage of shallow (limited by few layers only) convolutional neural networks (CNNs) to solve the video-based gender classification problem. Different architectures of shallow CNN are proposed, trained and tested using balanced and unbalanced static image datasets. The influence of diverse voting over confidences methods, applied for frame-by-frame gender classification of the video stream, is investigated for possible enhancement of the classification accuracy. The possibility of the grouping of shallow networks into ensembles is investigated; it has been shown that the accuracy may be more improved with the further voting of separate shallow CNN classification results inside an ensemble over a single frame or different ones.

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Broad Autoencoder Features Learning for Classification Problem

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.20211001oa10 ◽

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Classification Problem ◽

Activation Function ◽

Activation Functions ◽

Classification Problems ◽

Stacked Autoencoders ◽

Learned Features ◽

Sigmoid Functions ◽

Nonlinear Mappings

Activation functions such as Tanh and Sigmoid functions are widely used in Deep Neural Networks (DNNs) and pattern classification problems. To take advantages of different activation functions, the Broad Autoencoder Features (BAF) is proposed in this work. The BAF consists of four parallel-connected Stacked Autoencoders (SAEs) and each of them uses a different activation function, including Sigmoid, Tanh, ReLU, and Softplus. The final learned features can merge such features by various nonlinear mappings from original input features with such a broad setting. This helps to excavate more information from the original input features. Experimental results show that the BAF yields better-learned features and classification performances.

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On Using Transfer Learning For Plant Disease Detection

10.1101/2020.05.22.110957 ◽

2020 ◽

Author(s):

Abhinav Sagar ◽

J Dheeba

Keyword(s):

Neural Networks ◽

Image Classification ◽

Transfer Learning ◽

Plant Disease ◽

Deep Neural Networks ◽

Classification Problem ◽

Disease Detection ◽

Classification Problems ◽

Test Set ◽

Multi Class Classification

ABSTRACTDeep neural networks have been highly successful in image classification problems. In this paper, we show how deep neural networks can be used for plant disease recognition in the context of image classification. We have used a publicly available Plant Village dataset which has 38 classes of diseases. Hence the problem that we have addressed is a multi class classification problem. We have compared five different architectures including VGG16, ResNet50, InceptionV3, InceptionResNet and DenseNet169 as the backbones for our work. We found that ResNet50 which uses skip connections using a residual layer archives the best result on the test set. For evaluating the results, we have used metrics like accuracy, precision, recall, F1 score and class wise confusion metric. Our model achieves the best of results using ResNet50 with accuracy of 0.982, precision of 0.94, recall of 0.94 and F1 score of 0.94.

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