scholarly journals Gender classification using custom convolutional neural networks architecture

2020 ◽  
Vol 10 (6) ◽  
pp. 5758
Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Input Image ◽  
Gender Classification ◽  
Specific Task ◽  
Male And Female ◽  
Computation Efficiency ◽  
The Arts ◽  
Facial Images

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.

scholarly journals Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3508
Author(s):  
Mohammed Elhenawy ◽  
Huthaifa I. Ashqar ◽  
Mahmoud Masoud ◽  
Mohammed H. Almannaa ◽  
Andry Rakotonirainy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Training Time ◽  
Road Users ◽  
Smartphone Sensors ◽  
Vulnerable Road Users ◽  
And Behavior

As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes significant training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227 × 227 images to be used for AlexNet and SqueezeNet; and constructing 224 × 224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. Moreover, we trained resnet101 and shufflenet for a very short time using one epoch of data and then used them as weak learners, which yielded 98.49% classification accuracy. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

scholarly journals Age Invariant Face Recognition using Convolutional Neural Network

2018 ◽  
Vol 8 (4) ◽  
pp. 2126 ◽  
Author(s):  
Mrudula Nimbarte ◽  
Kishor Bhoyar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Classification Algorithms ◽  
The Arts ◽  
Single Structure ◽  
Facial Images ◽  
Selection Of

<span>In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH</span><span>(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH</span><span>(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier.</span>

scholarly journals Pengenalan Citra Wajah Dengan Menggunakan Transformasi Wavelet Diskrit dan Jaringan Saraf Tiruan Back-Propagation

2010 ◽  
Vol 1 (2) ◽  
pp. 691
Author(s):  
Suhendry Effendy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Input Image ◽  
Discrete Wavelet ◽  
Web Camera ◽  
Artificial Neural ◽  
Facial Images

This paper discusses the facial image recognition system using Discrete Wavelet Transform and back-propagation artificial neural network. Discrete Wavelet Transform processes the input image to obtain the essential features found on the face image. These features are then classified using an back-propagation artificial neural network for the input image to be identified. Testing the system using facial images in AT & T Database of Faces of 400 images comprising 40 facial images of individuals and web-camera catches as many as 100 images of 10 individuals. The accuracy of level of recognition on AT & T Database of Faces reaches 93.5%, while the accuracy of level of recognition on a web-camera capture images up to 96%. Testing is also done on image of AT & T Database of Faces with given noise. Apparently the noise in the image does not give meaningful effect on the level of recognition accuracy. 

scholarly journals Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Inventions ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 16
Author(s):  
Md. Mahbubul Islam ◽  
Nusrat Tasnim ◽  
Joong-Hwan Baek
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Pareto Frontier ◽  
Gender Classification ◽  
Learning Networks ◽  
Machine Learning Methods ◽  
Image Dataset ◽  
Internet Images ◽  
Learning Concept

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

scholarly journals Visual Recognition to Identify Helmet on Motorcycle Rider Using Convolutional Neural Network

2020 ◽  
Vol 14 (2) ◽  
pp. 89-94
Author(s):  
Kevin Alexander ◽  
Rayhan Ardiya Dwantara ◽  
Raihan Muhammad Naufal ◽  
Derwin Suhartono
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Supervised Learning ◽  
Visual Recognition ◽  
High Accuracy ◽  
Input Image ◽  
Ibm Watson ◽  
Motorcycle Rider ◽  
Motorcycle Accidents

The amount of motorcycle accidents is increasing each year. The main reason is that the riders do not wear a helmet. The research aims to minimize the accident by training the machine learning using the IBM Watson Studio. It trains the data about “wearing helmet” and “not wearing helmet”. The used method is Convolutional Neural Network (CNN). About 170 image datasets are used. CNN is conducted on the input image using a kernel or filter. The filter will multiply its values with the overlapping values of the image while also sliding and adding them all to produce a single value for each of them until the entire images have passed and finished. After CNN method is done, the researchers can classify the images by using supervised learning. It can identify whether the rider is wearing a helmet or not simply by scanning a picture on the street. The result shows high accuracy of 92.87%. The method can be used to minimize the percentage of motorcycle accidents caused by not wearing a helmet.

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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