Human Face Recognition using PCA Eigenfaces

Owais, Muhammad; Shaikh, Ammara; Jalal, Aireen Amir; Hassan, Muhammad Moiz

doi:10.1109/icetas48360.2019.9117489

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Then do the face image feature vector dimensionality reduction to find the key information for feature extraction. In the actual process of face recognition through PCA, PCA face recognition firstly preprocesses the face image, transforms the face into a row vector, and then obtains an average face through decentralization [5]. After that, its covariance matrix and its eigenvalues and eigenvectors are calculated, and then the eigenvalues are sorted and screened.…”

Section: Template-based Methodsmentioning

confidence: 99%

Research Advanced in the Face Recognition

Liu¹,

Wang²

2023

HSET

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Face recognition has always been a research hotspot in the field of computer vision, whose basic task is to recognize the identity of the face in the image. With the rapid development of computer technology, both the accuracy and speed of face recognition have achieved great breakthroughs, and face recognition technology has been widely used in the field such as cyber security, artificial intelligence and so on. In this paper, based on detailed literature research and analysis, we present a comprehensive review of the research work on face recognition technology. Specifically, we first analyze the difficulty of face recognition task from the human face feature extraction, which due to the variations within and between classes. We then introduce the main frameworks of face recognition from geometric feature-based method, template based method and model-based method. We further compare the performance of different algorithms on different common datasets. Finally, we give an outlook on the future research directions of face recognition.

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Section: Template-based Methodsmentioning

confidence: 99%

Research Advanced in the Face Recognition

Liu¹,

Wang²

2023

HSET

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“…Recently, PCA has been extensively used in fields such as machine learning, for example in data compression for deep learning, and algorithms for face recognition. [26][27][28][29] From the binarized image of the vibration displacement, the data to be input into the PCA were prepared as follows:…”

Section: I K mentioning

confidence: 99%

Automated mode chart generation for finite element analysis of piezoelectric resonators using principal component analysis based on clustering

Inoue,

Ohashi,

Arakawa

et al. 2024

Jpn. J. Appl. Phys.

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This paper presents a new clustering method for identifying the vibration mode shapes of piezoelectric resonators using image processing and principal component analysis (PCA). The finite element method (FEM) is an indispensable tool for analyzing the vibration-displacement distribution of resonators and suppressing spurious vibration modes. Although the FEM exports eigenfrequencies as a list, including spurious vibration modes, recognizing the vibration shape of each eigenfrequency requires manual operation by operators to view the displacement distribution and identify the vibration shapes. The proposed method consists of a preprocessing step based on a threshold selection before the PCA step. Preprocessing significantly simplifies the vibration displacement distribution and reduces the amount of data required for post-processing PCA. The mode chart of the AT-cut quartz crystal resonator obtained using the proposed method agreed with Koga's experimental results. The proposed method enables the generation of mode charts with reduced processing and computational resources.

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