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

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.

[1]  Jan K. Spelt,et al.  Materials Science and Engineering A , 2010 .

[2]  Muhammad Ali Ramdhani,et al.  Expert System for Deciding a Solution of Mechanical Failure in a Car using Case-based Reasoning , 2018 .

[3]  Tobias Lindahl.  Study of Local Binary Patterns , 2007 .

[4]  Bruce A. Draper,et al.  Computational Statistics and Data Analysis Introduction to Face Recognition and Evaluation of Algorithm Performance , 2022 .

[5]  Muhammad Ali Ramdhani,et al.  Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition , 2018 .

[6]  Muhammad Ali Ramdhani,et al.  Clustering the Verses of the Holy Qur'an using K-Means Algorithm , 2016 .

[7]  Manop Phankokkruad,et al.  An evaluation of face recognition algorithms and accuracy based on video in unconstrained factors , 2016, 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE).

[8]  Umi Rosyidah,et al.  PERBANDINGAN EUCLIDEAN DISTANCE DENGAN CANBERRA DISTANCE PADA FACE RECOGNITION , 2014 .

[9]  Ichsan Taufik,et al.  Implementasi Algoritma AES (Advanced Encryption Standard) 256 Bit Dan Kompresi Menggunakan Algoritma Huffman Pada Aplikasi Voice Recorder , 2018 .

[10]  Muhammad Ali Ramdhani,et al.  Social Media-Based Identifier for Natural Disaster , 2018 .

[11]  Ammar Rafiq,et al.  Comparative Analysis of Face Recognition Methodologies and Techniques , 2016 .

[12]  Muhammad Ali Ramdhani,et al.  Systematic Design of Expert System Using Unified Modelling Language , 2018 .

[13]  Della Gressinda Wahana.  IMPLEMENTASI DAN ANALISIS SISTEM PENGENALAN WAJAH DALAM RUANGAN PADA VIDEO MENGGUNAKAN METODE LNMF DAN NMFsc , 2015 .

[14]  Muhammad Ali Ramdhani,et al.  Expert system for predicting the early pregnancy with disorders using artificial neural network , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[15]  Muhammad Ali Ramdhani,et al.  Implementation of Nearest Neighbor using HSV to Identify Skin Disease , 2018 .

[17]  Iyos Kusmana.  Penggabungan Fitur Local Binary Patterns untuk Identifikasi Citra Tumbuhan Obat , 2011 .

[18]  Muhammad Ali Ramdhani,et al.  Incremental technique with set of frequent word item sets for mining large Indonesian text data , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[19]  Muhammad Ali Ramdhani,et al.  Web Scraping and Naïve Bayes Classification for Job Search Engine , 2018 .

[20]  Alwin M. Sambul,et al.  Implementasi Algoritma Pengenalan Wajah Untuk Mendeteksi Visual Hacking , 2017 .

[21]  Rifkie Primartha,et al.  Penerapan Enkripsi Dan Dekripsi File Menggunakan Algoritma Data Encryption Standard (DES) , 2011 .

[22]  Jumadi,et al.  Implementation of Mamdani Fuzzy Method in Employee Promotion System , 2018 .