Computer Vision Accuracy Analysis with Deep Learning Model Using TensorFlow

2020 ◽  
Author(s):  
T. Tritva Jyothi Kiran
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Model ◽  
Accuracy Analysis ◽  
Deep Learning Model

scholarly journals Masked Face Analysis via Multi-Task Deep Learning

2021 ◽  
Vol 7 (10) ◽  
pp. 204
Author(s):  
Vatsa S. Patel ◽  
Zhongliang Nie ◽  
Trung-Nghia Le ◽  
Tam V. Nguyen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Unified Framework ◽  
Improve Performance ◽  
Face Analysis ◽  
And Gender ◽  
Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

scholarly journals A Training Method for Low Rank Convolutional Neural Networks Based on Alternating Tensor Compose-Decompose Method

2021 ◽  
Vol 11 (2) ◽  
pp. 643
Author(s):  
Sukho Lee ◽  
Hyein Kim ◽  
Byeongseon Jeong ◽  
Jungho Yoon
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Model ◽  
Low Rank ◽  
Training Method ◽  
Training Stage ◽  
Fixed Structure ◽  
Deep Learning Model

Over the past decade, deep learning-based computer vision methods have been shown to surpass previous state-of-the-art computer vision techniques in various fields, and have made great progress in various computer vision problems, including object detection, object segmentation, face recognition, etc. Nowadays, major IT companies are adding new deep-learning-based computer technologies to edge devices such as smartphones. However, since the computational cost of deep learning-based models is still high for edge devices, research is being actively carried out to compress deep learning-based models while not sacrificing high performance. Recently, many lightweight architectures have been proposed for deep learning-based models which are based on low-rank approximation. In this paper, we propose an alternating tensor compose-decompose (ATCD) method for the training of low-rank convolutional neural networks. The proposed training method can better train a compressed low-rank deep learning model than the conventional fixed-structure based training method, so that a compressed deep learning model with higher performance can be obtained in the end of the training. As a representative and exemplary model to which the proposed training method can be applied, we propose a rank-1 convolutional neural network (CNN) which has a structure alternatively containing 3-D rank-1 filters and 1-D filters in the training stage and a 1-D structure in the testing stage. After being trained, the 3-D rank-1 filters can be permanently decomposed into 1-D filters to achieve a fast inference in the test time. The reason that the 1-D filters are not being trained directly in 1-D form in the training stage is that the training of the 3-D rank-1 filters is easier due to the better gradient flow, which makes the training possible even in the case when the fixed structured network with fixed consecutive 1-D filters cannot be trained at all. We also show that the same training method can be applied to the well-known MobileNet architecture so that better parameters can be obtained than with the conventional fixed-structure training method. Furthermore, we show that the 1-D filters in a ResNet like structure can also be trained with the proposed method, which shows the fact that the proposed method can be applied to various structures of networks.

scholarly journals Vehicular Security: Drowsy Driver Detection System

2021 ◽  
Vol 10 (5) ◽  
pp. 206-209
Author(s):  
Pranavi Pendyala ◽  
Aviva Munshi ◽  
Anoushka Mehra
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Data Collection ◽  
Detection System ◽  
Learning Model ◽  
Careful Observation ◽  
Non Invasive ◽  
Eye Closure ◽  
Deep Learning Model ◽  
Current Behaviour

Detecting the driver's drowsiness in a consistent and confident manner is a difficult job because it necessitates careful observation of facial behaviour such as eye-closure, blinking, and yawning. It's much more difficult to deal with when they're wearing sunglasses or a scarf, as seen in the data collection for this competition. A drowsy person makes a variety of facial gestures, such as quick and repetitive blinking, shaking their heads, and yawning often. Drivers' drowsiness levels are commonly determined by assessing their abnormal behaviours using computerised, nonintrusive behavioural approaches. Using computer vision techniques to track a driver's sleepiness in a non-invasive manner. The aim of this paper is to calculate the current behaviour of the driver's eyes, which is visualised by the camera, so that we can check the driver's drowsiness. We present a drowsiness detection framework that uses Python, OpenCV, and Keras to notify the driver when he feels sleepy. We will use OpenCV to gather images from a webcam and feed them into a Deep Learning model that will classify whether the person's eyes are "Open" or "Closed" in this article.

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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