scholarly journals A face recognition software framework based on principal component analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254965
Author(s):  
Peng Peng ◽  
Ivens Portugal ◽  
Paulo Alencar ◽  
Donald Cowan
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Software Framework ◽  
Software Developers ◽  
Practical Applications

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

scholarly journals Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

2020 ◽  
pp. 34-44
Author(s):  
Md. Mahbubul Alam ◽  
Md. Ashikur Rahman Khan ◽  
Zayed Us Salehin ◽  
Main Uddin ◽  
Sultana Jahan Soheli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Biometric System ◽  
Recognition Method ◽  
Person Identification ◽  
Multimodal Biometric System ◽  
Recognition Efficiency

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

scholarly journals Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

2018 ◽  
Vol 197 ◽  
pp. 03001
Author(s):  
Ichsan Taufik ◽  
Maya Musthopa ◽  
Aldy Rialdy Atmadja ◽  
Muhammad Ali Ramdhani ◽  
Yana Aditia Gerhana ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Recognition Time ◽  
Characteristic Extraction ◽  
Face Recognition System ◽  
Sample Test

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

scholarly journals Sistem Kontrol Akses Berbasis Real Time Face Recognition dan Gender Information

2015 ◽  
Vol 6 (2) ◽  
pp. 198
Author(s):  
Putri Nurmala ◽  
Wikaria Gazali ◽  
Widodo Budiharto
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Identification System ◽  
Face Recognition System ◽  
Gender Information ◽  
And Gender

Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systemsand it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information inthis system based on the Principal Component Analysis method (PCA). Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In thisaccess control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA ). The result achievedfrom the application design is the identification of a person’s face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy.

scholarly journals Multiscale Retinex Application to Analyze Face Recognition

2020 ◽  
Vol 5 (2) ◽  
pp. 217
Author(s):  
Supriyanto Supriyanto ◽  
Maisevli Harika ◽  
Maya Sri Ramadiani ◽  
Diena Rauda Ramdania
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Facial Image ◽  
Main Challenge ◽  
Face Recognition System

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.

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