An Experimental Performance Comparison of Widely Used Face Detection Tools
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Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ediciones Univ Salamanca
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Face detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, convolutional neural nowadays, face detection is commonly used in daily routines such as social media, and network camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query which face detection tool provides the best performance?. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.
Açıklama
Anahtar Kelimeler
face detection, face localization, computer vision, Tracking
Kaynak
Adcaij-Advances In Distributed Computing And Artificial Intelligence Journal
WoS Q DeÄŸeri
N/A
Scopus Q DeÄŸeri
Cilt
8
Sayı
3
