Skripsi

MULTI DIRECTIONAL FACE RECOGNITION DENGAN METODE CNN (CONVOLUTIONAL NEURAL NETWORK)

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Penilaian

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Face recognition is a field that has garnered significant attention in the development of artificial intelligence systems. In this research, we focus on face recognition from multiple viewpoints using the Convolutional Neural Network (CNN) method. Conventional approaches to face recognition often overlook variations in face position and orientation, leading to unsatisfactory performance in real-world scenarios. To address this challenge, we propose an approach that utilizes a convolutional neural network, specifically the Convolutional Neural Network (CNN), which has proven successful in various complex pattern recognition tasks. The proposed method consists of several stages. First, we collect a dataset that includes various variations in face position and orientation. This dataset encompasses rotated, tilted, and scaled faces. Next, we train the CNN model using the collected dataset. The training process involves hierarchical feature extraction using convolutional and pooling layers to recognize face patterns from multiple viewpoints. After training the CNN model, we conduct testing using an unseen test dataset consisting of previously unseen faces. We evaluate the model's performance based on commonly used face recognition metrics such as accuracy, precision, and recall. We also compare our model's performance with other face recognition methods in the literature. Our research findings demonstrate that the proposed CNN method successfully recognizes faces from multiple viewpoints with high accuracy. We also discover that the CNN model has an advantage in handling variations in face position and orientation compared to conventional methods. These results highlight the potential use of CNN methods in multi-view face recognition. This research has significant implications for the development of more advanced and reliable face recognition systems. Its findings can be applied in various practical applications, including security, surveillance, and individual identification in images or videos.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307005653	T12506	T1250622023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1250622023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 86 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.607

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Tampilan Antar Muka (Interface)

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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