Autonomous Recognition System for Traffic Signs Detection from Camera Images

Hercík, Radim; Srovnal, Vilem; Stankuš, Martin; Slaby, Roman; Vaňuš, Jan

doi:10.3182/20130925-3-cz-3023.00095

Cited by 2 publications

(1 citation statement)

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“…Matrik bobot yang diperoleh digunakan untuk mencari nilai threshold yang menjadi ambang yang harus dilewati oleh data pengujian,untuk membuktikan nilai data pengujian menjadi data yang dikenali nilai threshold dihitung melalui perhitungan jarak tiap bobot menggunakan eucladean distance. [10] 2.7 Pengujian wajah Pengujian citra wajah akan membandingkan jarak antara nilai citra wajah training dengan nilai setiap citra pelatihan pada ruang wajah, proses dilakukan dengan cara menentukan jarak minimum, jika jarak citra uji lebih kecil dari threshold maka wajah akan dikenali tetapi jika jarak uji lebih besar dari threshold maka citra uji tidak mengenali [11]. Seperti pada flowchart yang disajikan pada Gambar 9 berikut: Tingkat akurasi pada deteksi pengenalan wajah perokok adalah 92.5% dimana ada 34 wajah perokok yang berhasil terdeteksi dan 6 wajah perokok yang tidak terdeteksi hal ini dipengaruhi oleh beberapa faktor diantaranya adalah tingkat intesitas cahaya disaat pengambilan gambar.…”

Section: Pengenalan Wajahunclassified

Identifikasi Pengenalan Wajah Perokok Menggunakan Metode Principal Component Analysis

yusni¹,

Zaini

2020

RESTI

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Cigarettes are one of the biggest contributors to preventable causes of death in society. Cigarette smoke contains various chemicals that can cause various diseases such as chronic coughs, lung cancer, and other health problems. Cigarette smoke not only harms the health of the smoker itself but also the health of others. Sometimes written warnings about smoking bans are often not followed by active smokers. This study aims to identify smokers 'facial recognition in order to recognize and identify smokers' faces who do not obey the rules by using dimensional reduction techniques oriented to the Principal component Analysis (PCA) method. Principal Component Analysis will later be integrated with the Eigenface and Eucladean analysis algorithms to reduce the image size in obtaining the best value vectors to simplify the face image in the input image space and look for the threshold value which is the threshold that the test data must pass so that it can prove the data value. testing becomes recognizable data through the calculation of the distance for each weight. In this study, there were 8 smoker faces with 5 different facial poses that were tested for 40 face recognition experiments and resulted in 34 correct smoker face recognition and 6 wrong smoker face recognition with an accuracy rate of 92.5% and a long face recognition process time of 80. second. This test has proven that the Eigenface and Euclidean distance in the Principal Component Analysis (PCA) are able to handle and recognize smoker's facial image data well.

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Section: Pengenalan Wajahunclassified