Biometric identification system based on Principal Component Analysis

Lionnie, Regina; Alaydrus, Mudrik

doi:10.1109/icmsa.2016.7954309

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…Calce et al [28] applied principal component analysis to the standard evaluation of the osteoarthritis. Lionnie et al [29] established a biometric recognition pattern system, in which principal component analysis extracts features in the mathematical and statistical solution. The cross-validation proved the validity of the fusion method.…”

Section: Correlation Analysis Methodsmentioning

confidence: 99%

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

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Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

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Section: Correlation Analysis Methodsmentioning

confidence: 99%

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

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“…PCA is a features extraction and reduction method that transforms the original data into a new set of data that can be represented in a lower dimension [31]. In the transformed data, the first several data elements contain the most relevant information that may reveal the most significant characteristics of the data that were obscured or hidden before the transformation [31]. This is done by creating a set S containing M feature vectors of size N, where M is the number of images, and N = image height × image width.…”

Section: Pca-based Featuresmentioning

confidence: 99%

Dynamic Feature Subset Selection for Occluded Face Recognition

Alsaedi¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

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“…The other feature extraction method for face images was PCA, which another widely used feature extraction and reduction method for face recognition [54]. It transforms the original data into a new less dimension set of data containing the most relevant information that may reveal characteristics of the data that were once hidden before the transformation [55]. This is done by constructing M feature vector from M training samples, each vector of size N, where N = image height × image width.…”

Section: • Pca-based Featuresmentioning

confidence: 99%

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Ghalandari¹,

Mahariq²,

Ghadak³

et al. 2022

Computers, Materials &Amp; Continua

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Biometric recognition refers to the process of recognizing a person's identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems' performance, such as occlusion, face poses, and noise in voice data. In this paper, we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal biometrics. The proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition performance. The proposed dynamic fusion was achieved using face and voice biometrics, where face features were extracted using principal component analysis (PCA), and Gabor filters separately, whilst voice features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs). Here, the facial data quality assessment of face images is mainly based on the existence of occlusion, whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio (SNR) as per the existence of noise. To evaluate the performance of the proposed algorithms, several experiments were conducted using two combinations of three different databases, AR database, and the extended Yale Face Database B for face images, in addition to VOiCES database for voice data. The obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using standard fusion methods.

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Biometric identification system based on Principal Component Analysis

Cited by 14 publications

References 19 publications

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Dynamic Feature Subset Selection for Occluded Face Recognition

Dynamic Audio-Visual Biometric Fusion for Person Recognition

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