Effect of Distance Measures on the Performance of Face Recognition Using Principal Component Analysis

Borade, Sushma Niket; Deshmukh, Ratnadeep R.; Shrishrimal, Pukhraj P.

doi:10.1007/978-3-319-23036-8_50

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…For the estimation of the confidence, the distances between the neighbors in original and low-dimensional spaces can be identified by different distance measures. Several studies have been conducted to analyze the performance of the algorithms that are affected by the choice of distance measures, such as the k-nearest neighbor (KNN) classifier 12 , 13 , image recognition 14 , and some clustering algorithms 15 , 16 . All these studies conclude that the choice of distance measures has a substantial impact on the performance of these algorithms since they found considerable variations in the results for different distances.…”

Section: Methodsmentioning

confidence: 99%

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data

Yigin

Saygılı

2023

Sci Rep

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Arguably one of the most famous dimensionality reduction algorithms of today is t-distributed stochastic neighbor embedding (t-SNE). Although being widely used for the visualization of scRNA-seq data, it is prone to errors as any algorithm and may lead to inaccurate interpretations of the visualized data. A reasonable way to avoid misinterpretations is to quantify the reliability of the visualizations. The focus of this work is first to find the best possible way to predict sample-based confidence scores for t-SNE embeddings and next, to use these confidence scores to improve the clustering algorithms. We adopt an RF regression algorithm using seven distance measures as features for having the sample-based confidence scores with a variety of different distance measures. The best configuration is used to assess the clustering improvement using K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based on Adjusted Rank Index (ARI), Normalized Mutual Information (NMI), and accuracy (ACC) scores. The experimental results show that distance measures have a considerable effect on the precision of confidence scores and clustering performance can be improved substantially if these confidence scores are incorporated before the clustering algorithm. Our findings reveal the usefulness of these confidence scores on downstream analyses for scRNA-seq data.

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Section: Methodsmentioning

confidence: 99%

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data

Yigin

Saygılı

2023

Sci Rep

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“…The LFW dataset has been extensively used in facial recognition research from a variety of angles. [18][19][20]. Second dataset is face94 with more than 3000 facial images that has been used in research [21][22][23].…”

Section: Methodsmentioning

confidence: 99%

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Kristanto

Riadi

Prayudi

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.

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“…Distances d (x, y) between these two vectors are mathematically expressed as follows in Eq. ( 20) [69,70]:…”

Section: Matchingmentioning

confidence: 99%

A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

Shaban¹,

Ahmed²,

Elsheweikh³

2023

Intelligent Automation &Amp; Soft Computing

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With the rapid spread of the coronavirus epidemic all over the world, educational and other institutions are heading towards digitization. In the era of digitization, identifying educational e-platform users using ear and iris based multimodal biometric systems constitutes an urgent and interesting research topic to preserve enterprise security, particularly with wearing a face mask as a precaution against the new coronavirus epidemic. This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations (E-exams) during the COVID-19 pandemic. The proposed system comprises four steps. The first step is image preprocessing, which includes enhancing, segmenting, and extracting the regions of interest. The second step is feature extraction, where the Haralick texture and shape methods are used to extract the features of ear images, whereas Tamura texture and color histogram methods are used to extract the features of iris images. The third step is feature fusion, where the extracted features of the ear and iris images are combined into one sequential fused vector. The fourth step is the matching, which is executed using the City Block Distance (CTB) for student identification. The findings of the study indicate that the system's recognition accuracy is 97%, with a 2% False Acceptance Rate (FAR), a 4% False Rejection Rate (FRR), a 94% Correct Recognition Rate (CRR), and a 96% Genuine Acceptance Rate (GAR). In addition, the proposed recognition system achieved higher accuracy than other related systems.

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Effect of Distance Measures on the Performance of Face Recognition Using Principal Component Analysis

Cited by 9 publications

References 6 publications

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

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