Image processing effects on the deep face recognition system

Zeng, Jinhua; Qiu, Xiulian; Shu, Shi

doi:10.3934/mbe.2021064

Cited by 20 publications

(10 citation statements)

References 33 publications

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“…In this study, the proposed technique uses GF to get rid of the noise exists in the IVUS images. The GF approach is used for image filtering process that removes the noise content and enhances the image quality [19]. A 2D GF technique is used to improve the quality of the dental system.…”

Section: Image Preprocessing Using Gf Techniquementioning

confidence: 99%

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

2023

IJIES

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Intravascular ultrasound (IVUS) is suitable for evaluating plaque and lesion morphology features and helps to make clinical decisions during the treatment of coronary artery disease (CAD). IVUS remain a gold standard in accessing atherosclerosis plaques, coronary lesions, and stenosis. Even though plaque classification by IVUS is essential for risk stratification, frame-by-frame analysis of an entire vascular segment is labor intensive. In recent times, in the field of deep learning (DL), convolutional neural network (CNN) has been intended to adaptively and automatically determine the spatial hierarchy through backpropagation. The study develops an optimal deep transfer learning based on atherosclerotic plaque and calcification on intravascular ultrasound images (ODTL-APCIUI) technique. The presented ODTL-APCIUI technique aims to classify atherosclerotic plaque and calcification. To accomplish this, the presented ODTL-APCIUI technique preprocesses the IVUS images by Gaussian filtering (GF) technique. In addition, U2Net model is applied for the segmentation process with Adam optimizer based hyperparameter tuning. Moreover, the ODTL-APCIUI technique uses DenseNet-169 model for feature extraction purposes. Furthermore, the ODTL-APCIUI technique exploits stacked autoencoder (SAE) for classification process. Finally, Harris Hawks optimization (HHO) algorithm is exploited for the hyperparameter adjustment of the SAE approach. The performance assessment of ODTL-APCIUI algorithm is tested using medical images and the results are investigated under different metrics. The experimental outcomes demonstrated that the ODTL-APCIUI technique has gained better performance with maximum accuracy of 97.19%, precision of 94.09%, sensitivity of 97.17%, specificity of 97.19%, and F-score of 92.52%.

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Section: Image Preprocessing Using Gf Techniquementioning

confidence: 99%

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

2023

IJIES

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“…[ [10][11][12] It is based on face detection processes to isolate a human face within an input image that contains a human face.…”

Section: Category Paper Id Descriptionmentioning

confidence: 99%

“…The face recognition process [10][11][12] starts with a face detection process to isolate a human face within an input image that contains a human face. Then, the detected face is pre-processed to obtain a low-dimensional representation.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms

et al. 2021

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Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.

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“…can hamper the system's performance [10]. Image denoising can be used to further increase the accuracy of the system [11]. But the efficacy of these systems is affected when these are applied to recognize masked faces.…”

Section: Introductionmentioning

confidence: 99%

Masked-face recognition using deep metric learning and FaceMaskNet-21

Golwalkar

Mehendale

2022

Appl Intell

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The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. The conventional face recognition systems used for security purposes have become ineffective in the current situation since the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. We have proposed a system that uses the deep metric learning technique and our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, static video files. We achieved a testing accuracy of 88.92% with an execution time of fewer than 10 ms. The ability of the system to perform masked face recognition in real-time makes it suitable to recognize people in CCTV footage in places like malls, banks, ATMs, etc. Due to its fast performance, our system can be used in schools and colleges for attendance, as well as in banks and other high-security zones to grant access to only the authorized ones without asking them to remove the mask.

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Image processing effects on the deep face recognition system

Cited by 20 publications

References 33 publications

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms

Masked-face recognition using deep metric learning and FaceMaskNet-21

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