Pengenalan Personal Menggunakan Citra Tampak Atas pada Lingkungan Cashierless Strore


  • Bambang Nurcahyo Prastowo Departemen Ilmu Komputer dan Elektronika, Universitas Gadjah Mada
  • Nur Achmad Sulistyo Putro Departemen Ilmu Komputer dan Elektronika, Universitas Gadjah Mada
  • Oktaf Agni Dhewa Departemen Ilmu Komputer dan Elektronika, Universitas Gadjah Mada
  • Ach Maulana Habibi Yusuf Departemen Ilmu Komputer dan Elektronika, Universitas Gadjah Mada




Personal recognition with image processing techniques from the side view has the disadvantage of being applied to the cashierless store environment, namely inaccurate recognition or identification when personal collisions occur. To overcome this, the image capture method is used from the top-view. Personal recognition method through the top-view image using the Haar Cascade Classifier method. 1420 positive images and 2170 negative images are used to find features that are considered suitable for recognizing objects using the Adaptive Boosting (Adaboost) method. Tests were carried out on 100 test data by varying the parameters of min_neighbors (3.4, and 5) and the size of the dataset window (25x25, 35x35, 45x45 pixels). Personal recognition testing gets the highest accuracy of 89.9% with the parameters used are min_neighbors 5 and the size of the 25x25 pixel dataset in the detection parameter size of min_size 140x140 pixels.
Keywords: Person recognition, image processing, cashierless store


Pengenalan personal dengan teknik pengambilan citra dari tampak samping memiliki kelemahan untuk diterapkan pada lingkungan cashierless store yaitu tidak akuratnya pengenalan atau identifikasi saat terjadi tubrukan antar personal. Untuk mengatasi hal tersebut maka dipakailah metode pengambilan citra dari tampak atas. Metode pengenalan personal melalui citra tampak atas menggunakan metode Haar Cascade Classifier. Digunakan 1420 citra positif dan 2170 citra negatif untuk menemukan fitur-fitur yang dianggap cocok untuk mengenali objek dengan menggunakan metode Adaptive Boosting (Adaboost). Pengujian dilakukan terhadap data tes sebanyak 100 citra dengan menvariasikan parameter min_neighbors (3,4, dan 5) dan ukuran window dataset (25x25, 35x35, 45x45 piksel). Pengujian pengenalan personal mendapatkan akurasi tertinggi sebesar 89,9% dengan parameter yang dipakai yaitu min_neighbors 5 dan ukuran window dataset 25x25 piksel pada parameter ukuran pengujian min_size 140x140 piksel.
Kata Kunci: pengenalan personal, pengolahan citra, cashierless store


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