Searched over 200M research papers
9 papers analyzed
These studies suggest that facial recognition algorithms include deep convolutional neural networks (DCNN), 2D-HOG and 2DPCA, CNN, PCA, LDA, ICA, EBGM, EBGM-Face, HOG, and Haar-Cascade.
19 papers analyzed
Facial recognition (FR) technology has become increasingly important in various applications, from security systems to commercial identification tools. Researchers have developed numerous algorithms to enhance the accuracy and robustness of FR systems under different conditions. This synthesis aims to present the key insights from multiple research papers on the available facial recognition algorithms.
Deep Convolutional Neural Networks (DCNN)
Histogram of Oriented Gradients (HOG)
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)
Support Vector Machine (SVM)
K-Nearest Neighbors (KNN)
Eigenfaces with PCA
Elastic Bunch Graph Matching (EBGM)
Independent Component Analysis (ICA)
Discrete Cosine Transform (DCT)
Facial recognition technology employs a variety of algorithms, each with its strengths and weaknesses. DCNNs and HOG combined with 2DPCA are highly effective for feature extraction and recognition tasks. Traditional methods like PCA, LDA, and Eigenfaces continue to be relevant, especially when fused for improved performance. SVM and KNN are standard machine learning algorithms frequently used in FR systems. Advanced methods like EBGM and ICA offer robustness against facial variations, while DCT-based algorithms handle occlusions and illumination changes effectively. The choice of algorithm often depends on the specific requirements and conditions of the application.
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