When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

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Face Recognition Based on Lightweight Convolutional Neural Networks

Information ◽

10.3390/info12050191 ◽

2021 ◽

Vol 12 (5) ◽

pp. 191

Author(s):

Wenting Liu ◽

Li Zhou ◽

Jie Chen

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Networks ◽

Network Architecture ◽

Recognition Task ◽

Training Method ◽

The Face ◽

And Storage ◽

And Training

Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are difficult to deploy on mobile devices or embedded terminals. In this paper, we propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized in terms of the network architecture and training pattern on the basis of MobileFaceNet. Regarding the network architecture, we introduce the Squeeze-and-Excitation (SE) block and propose three improved structures via a channel attention mechanism—the depthwise SE module, the depthwise separable SE module, and the linear SE module—which are able to learn the correlation of information between channels and assign them different weights. In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the deep network for face recognition. Finally, we obtained high-precision and lightweight face recognition models with fewer parameters and calculations that are more suitable for applications. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed methods.

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Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks

Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-6690-9.ch013 ◽

2021 ◽

pp. 241-265

Author(s):

Wencan Zhong ◽

Vijayalakshmi G. V. Mahesh ◽

Alex Noel Joseph Raj ◽

Nersisson Ruban

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Face Recognition ◽

Facial Expression ◽

Convolutional Neural Networks ◽

Zernike Moments ◽

The Other ◽

Recognition Systems ◽

Facial Images ◽

Surveillance Applications

Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.

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Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.164168 ◽

2019 ◽

Vol 7 (6) ◽

pp. 164-168

Author(s):

Kshitij Tripathi ◽

Rajendra G. Vyas ◽

Anil K. Gupta

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Novel Approach

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CONVOLUTIONAL NEURAL NETWORKS, ANALYTICAL ALGORITHMS, AND PERSONALIZED HEALTH CARE: EMBRACING THE MASSIVE DATA ANALYSIS CAPABILITIES OF DEEP LEARNING ARTIFICIAL INTELLIGENCE SYSTEMS TO COMPLEMENT AND IMPROVE MEDICAL SERVICES

American Journal of Medical Research ◽

10.22381/ajmr5220187 ◽

2018 ◽

Vol 5 (2) ◽

pp. 52 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Health Care ◽

Deep Learning ◽

Data Analysis ◽

Convolutional Neural Networks ◽

Massive Data ◽

Personalized Health ◽

Personalized Health Care ◽

Artificial Intelligence Systems

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Applied Sciences ◽

10.3390/app11052284 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2284

Author(s):

Asma Maqsood ◽

Muhammad Shahid Farid ◽

Muhammad Hassan Khan ◽

Marcin Grzegorzek

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Red Blood Cells ◽

Convolutional Neural Networks ◽

Blood Cells ◽

Superior Performance ◽

Learning Models ◽

Infected Female ◽

The World ◽

Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

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Efficient downscaling of satellite oceanographic data with convolutional neural networks

SIGSPATIAL Special ◽

10.1145/3447994.3448011 ◽

2021 ◽

Vol 12 (3) ◽

pp. 46-47

Author(s):

Nikita Saxena

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Super Resolution ◽

Sea Surface ◽

Clear Sky ◽

Oceanographic Data ◽

Computationally Expensive ◽

Sky Conditions ◽

Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

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