Hand Human Recognition Berdasarkan Geometri Telapak Tangan Menggunakan Principal Component Analysis

Abstract

Self-recognition system is a system that can be used to recognize someone's identity that can be done automatically using a computer. Automatic self-recognition can be done by using body parts or human behavior is known as biometrics. Biometrics is a self-recognition technology that uses body parts or behavior from humans. There are several ways for general biometrics that are often used for self-recognition, such as fingerprints, iris, face, face, voice, signature, hand geometry, and palmprint. Hand geometry is one of the biometrics owned by humans that can describe the geometric structure of a person's hand. The system contained in this study is a palm recognition system that uses feature-based extraction based on Principal Component Analysis (PCA). This technique involves taking the main components of the palm database. To find out the accuracy of the palm recognition system designed in this study, a system trial has been carried out using 21 palm image images from the database. From the results of this test, the results of the system are 52.38% correctly recognizing input images.

Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network (CNN)

“…Biometrik memiliki ciri kerja dengan mengukur karakteristik pembeda pada badan atau perilaku seseorang tersebut dengan membandingkan karakteristik yang sebelumnya telah disimpan pada suatu database. Terdapat beberapa cara untuk biometrik umum yang sering dipakai untuk pengenalan diri, seperti sidik jari (fingerprint), selaput pelangi (iris), wajah (face), suara (voice), tanda tangan (signature), geometri tangan (hand geometry) dan telapak tangan (palmprint) [3].…”