Recognition system for facial expression by processing images with deep learning neural network

Abstract

The recognition systems of patterns in images are mechanisms that filter the information that provides an image to highlight the area of interest for the user. Usually, these mechanisms are based on mathematical transformations that allow the processor to perform interpretations based on the geometry or shape of the image. However, the strategies that implement mathematical transformations are limited, since the effectiveness of these techniques is reduced by changing the morphology or resolution of the image. This paper presents a partial solution to this limitation with a digital image processing technique based on a deep learning neural network (DNN). This technique incorporates a mechanism that allows the DNN to determine the facial expression of a person, based on the segmented information of the image of their face. By segmenting the image and processing its characteristics in parallel, the proposed technique increases the effectiveness of recognizing facial gestures in different images even when modifying their characteristics.

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“…Pengolahan citra merupakan sebuah teknologi yang p-ISSN: 2502-7131 e-ISSN: 2502-714x digunakan untuk memproses citra melalui proses komputasi [9]. Pengolahan citra juga disebutkan sebagai sebuah teknologi visual yang mampu menganalisis objek citra digital [10].…”

“…Pengujian ini dilakukan dengan menggunakan kondisi pencahayaan 10-1000 Lux, jarak ideal 3-200 Cm, dan kecepatan gerak objek sebesar 4-10 Detik. Berikut merupakan hasil percobaan dari 10 data uji yang telah dilakukan : Dari data pengujian yang telah dilakukan pada data uji yang ada, maka diperoleh nilai akurasi sebagai berikut : Accuracy (10) = (∑Uji / ∑Data) x 100% = (10 / 10) x 100% = 100%…”

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