Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Arnia, Fitri; Oktiana, Maulisa; Saddami, Khairun; Munadi, Khairul; Roslidar, Roslidar; Pradhan, Biswajeet

doi:10.3390/s21134575

Cited by 8 publications

(1 citation statement)

References 41 publications

(75 reference statements)

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“…The whole process of returning books in the library is cumbersome, and book returners often forget to carry valid borrowing certificates, which lead to the failure of book return and wastes the time and energy of book returners [3]. Therefore, some university administrators and technical experts propose applying advanced face recognition technology to the library return process and building a university library intelligent return model, so that book returners do not need to carry additional documents and only use their social biometric information and the book return process can be completed [4]. However, the recognition accuracy of algorithms used in the same type of products in the current market still needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

Shi¹

2023

IJACSA

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With the improvement in the quality of university education in China, the behavior of college students and school teachers to borrow and return books in the library is becoming more and more frequent. In peak periods of returning books, managers cannot even assist in returning books in time. Therefore, this research uses kernel function, multi-dimensional principal component analysis method, and multi-dimensional linear discriminant analysis method to construct a new face recognition algorithm for the automatic return of books in the university library. The test results show that the XT_2D_PL algorithm designed in this study has a face recognition rate of 96.8%. When the number of face samples of each type in the test sample set is 11, and when the number of feature dimensions is 14, the recognition rate of 96.3% reaches the highest level. However, if the sample to be processed is 500 pictures, the calculation speed is 1.072ms/per photo, higher than most comparison algorithms. The proposed face recognition algorithm has high recognition accuracy on the library face data; the calculation speed meets the needs of practical applications, and has certain practical promotion potential.

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Section: Introductionmentioning

confidence: 99%

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

Shi¹

2023

IJACSA

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Surface defect detection of Si3N4 ceramic bearing ball based on improved homomorphic filter-Gaussian filter coupling algorithm

Liao

Cui

et al. 2022

AIP Advances

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Considering the tiny surface defects of Si3N4 ceramic bearing balls and the low accuracy of defect detection using a single traditional algorithm, we find that the performance of the aerospace mechanical power system is poor. A coupling algorithm based on the improved homomorphic filter and Gaussian filter is proposed. The Si3N4 ceramic bearing ball nondestructive testing platform is established, by which the surface defect images of Si3N4 ceramic bearing balls are collected. The image acquisition card obtains the image information and then transmits it to the image storage module. The image processing module handles surface defect images. The gray transformation algorithm is used to complete image preprocessing. Gaussian noise in images is filtered out using the Gaussian filter algorithm. The homomorphic filtering algorithm is used to enhance the high frequency component, compress the low frequency component, and filter out the convolution noise and promiscuous signal. The contrast of the defect part has been reinforced using the coupling algorithm. It turns out that the accuracy of the coupling algorithm is 100%, 96.7%, 98.9%, and 94.4%.

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Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

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Due to their ease-of-use, biometric verification methods to control access to digital devices have become ubiquitous. Many rely on supervised machine learning, a process that is notoriously datahungry. At the same time, biometric data is sensitive from a privacy perspective, and a comprehensive review from a data set perspective is lacking. In this survey, we present a comprehensive review of multimodal face data sets (e.g., data sets containing RGB color plus other channels such as infrared or depth). This follows a trend in both industry and academia to use such additional modalities to improve the robustness and reliability of the resulting biometric verification systems. Furthermore, such data sets open the path to a plethora of additional applications, such as 3D face reconstruction (e.g., to create avatars for VR and AR environments), face detection, registration, alignment, and recognition systems, emotion detection, anti-spoofing, etc. We also provide information regarding the data acquisition setup and data attributes (ethnicities, poses, facial expressions, age, population size, etc.) as well as a thorough discussion of related applications and state-of-the-art benchmarking. Readers may thus use this survey as a tool to navigate the existing data sets both from the application and data set perspective. To existing surveys we contribute, to the best of our knowledge, the first exhaustive review of multimodalities in these data sets.

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Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Cited by 8 publications

References 41 publications

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

Surface defect detection of Si3N4 ceramic bearing ball based on improved homomorphic filter-Gaussian filter coupling algorithm

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

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