Face Recognition Using Principal Component Analysis

Rani, Geeta; Sakthimohan., M; Suresh, Manoharan Pon; Abhiram, Madhurapantula; Surya, Kandregula Jai; Kumar, Balasaraswathi Yugandher Adiki Nithin

doi:10.1109/icacite53722.2022.9823434

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…Beberapa algoritma, teknik maupun metode telah banyak digunakan dalam eksperimen pengenalan wajah seperti algoritma PCA [7], [8], viola jones [9], wavelet [2], CNN [10], [11] , haar cascade [11], [12]. Beberapa contoh tersebut memiliki hasil pengenalan wajah yang sesuai, tingkat akurasi tinggi dan mampu mendeteksi wajah sesuai dengan citra data latih.…”

Section: Iunclassified

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Riski,

Utomo

2024

JPIT

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Current facial recognition system utilizes the Principal Component Algorithm (PCA) and the Bounding Box method to recognize facial locations based on brightness levels. The problem that was found in the experiment was that unclear or illuminated light factors could cause inaccuracies in facial area recognition. PCA is an algorithm capable of performing dimensional reduction to recognize the face area. The recognition process involves image pre-processing, PCA analysis to produce vectors, and application of a Bounding Box to focus on critical areas. This research contributes to the development of reliable and efficient facial recognition systems, potentially applied in security and access management. The experiment used the Grimace dataset using the .jpg format, with tests on normal brightness and -50 decreases in brightness level. At the decrease in -50, the result shows that the smallest distance value is 3540.1, and the greatest distance is 6849.4 with the average value being 5810.110. face recognition results can recognize face images with the original image

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Section: Iunclassified

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Riski,

Utomo

2024

JPIT

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“…PCA is a widely used method for feature extraction over a several disciplines, including but not limited to pattern recognition, computer vision, and image processing. The original data are transformed using a linear method into a reduced-dimensional subspace, in which the directions with the highest variance in the data are retained, and the others are discarded (Rani et al, 2022).…”

Section: Feature Extraction and Recognitionmentioning

confidence: 99%

“…In this approach, three different hand-crafted methods have been used in order to do the face recognition task, namely Principal Component Analysis (PCA) (Rani et al, 2022), Local Binary Patterns (LBP) (Kulkarni et al, 2017) and Histogram of Oriented Gradients (HOG) (Ahamed et al, 2018). All the experimental works are conducted on stateof-the-art datasets, namely Cross-Age Labeled Faces in the Wild (CALFW) (Zheng et al, 2017) and Masked Labeled Faces in the Wild (MLFW) (Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Masked and Unmasked Face Recognition on Unconstrained Facial Images Using Hand-Crafted Methods

TORBATI,

TOYGAR

2023

KSU J. Eng. Sci.

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In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep learning systems. A low-cost system is intended to be constructed for recognizing masked faces and compares its performance to that of face recognition systems that do not use masks. The proposed method fuses hand-crafted methods using feature-level fusion strategy. This study compares the performance of masked and unmasked face recognition systems. The experiments are undertaken on two publicly accessible datasets for masked face recognition: Masked Labeled Faces in the Wild (MLFW) and Cross-Age Labeled Faces in the Wild (CALFW). The best accuracy is achieved as 94.8% on MLFW dataset. The rest of the results on different train and test sets from CALFW and MLFW datasets are encouraging compared to the state-of-the-art models.

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Deep Learning-Powered Face Recognition Attendance System: A Novel Approach to Automated Prediction

Sornalakshmi,

Abinash,

Gopalakrishnan

et al. 2023

2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS)

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Face Recognition Using Principal Component Analysis

Cited by 14 publications

References 4 publications

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Masked and Unmasked Face Recognition on Unconstrained Facial Images Using Hand-Crafted Methods

Deep Learning-Powered Face Recognition Attendance System: A Novel Approach to Automated Prediction

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