Illumination-robust face recognition based on deep convolutional neural networks architectures

Bendjillali, Ridha Ilyas; Beladgham, Mohammed; Merit, Khaled; Taleb‐Ahmed, Abdelmalik

doi:10.11591/ijeecs.v18.i2.pp1015-1027

Cited by 48 publications

(31 citation statements)

References 26 publications

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“…The proposed system presents an efficient algorithm for detecting and recognizing a human face from live video by applying the Viola-Jones algorithm [3,11] depending on python. The flow chart for the proposed software system is as shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Face recognition using viola-jones depending on python

Ismael¹,

Stanciu

2020

IJEECS

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In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:Eigenfaces → createEigenFaceRecognizer()Fisherfaces → createFisherFaceRecognizer()Local Binary Patterns Histograms → createLBPHFaceRecognizer()Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.

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Section: Methodsmentioning

confidence: 99%

Face recognition using viola-jones depending on python

Ismael¹,

Stanciu

2020

IJEECS

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“…These layers are not supposed to provide estimates of classification. The FC layer is used at this stage to identify the input picture according to the training set by looking at the features [19,20]. After each convolutional layer, the ReLU activation layer is conventionally used.…”

Section: Figure 2 Main Structure Of Cnnmentioning

confidence: 99%

“…ResNet's fundamental breakthrough was that it allowed us to successfully train incredibly deep neural networks with 150+layers. The ResNet 50 proposed in [25] with 50 residual network layers by He et al The height of the convolution layers is 33 filters and this model has an input size of 224*224 [20]. Each model is used to train the images with SGDM and max Epoch of 10 with mini batch size=6 and initial learning rate 1e -4 .…”

Section: Residual Network Architectures (Resnet)mentioning

confidence: 99%

Detection of citrus leaf diseases using a deep learning technique

Luaibi

Salman

Miry

2021

IJECE

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The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

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“…DL techniques, such as convolutional neural networks (CNNs), have already influenced a wide range of signal processing activities within traditional and new advanced areas, including key aspects of machine learning and artificial intelligence [1]. In particular, CNNs showed superior performance in face detection applications [2,3]. Furthermore, DL has made considerable progress in detection and classification of the hand gestures for implementation into the human computer interaction (HCI) technologies [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based static hand gesture recognition

Satybaldina

Kalymova

2021

IJEECS

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

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Illumination-robust face recognition based on deep convolutional neural networks architectures

Cited by 48 publications

References 26 publications

Face recognition using viola-jones depending on python

Face recognition using viola-jones depending on python

Detection of citrus leaf diseases using a deep learning technique

Deep learning based static hand gesture recognition

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