Realtime face matching and gender prediction based on deep learning

Surinwarangkoon, Thongchai; Hoang, Vinh Truong; Vafaei-Zadeh, Ali; Hendi, Hayder; Meethongjan, Kittikhun

doi:10.11591/ijece.v13i4.pp4068-4075

Cited by 1 publication

(1 citation statement)

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“…In paper [ 10 ], the evaluated Kullback–Leibler divergence was used for gender classification in images of human faces. Paper [ 11 ] presents an algorithm for face matching and gender prediction. The face identification process uses distance metrics.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes

Maciura¹,

Cieplak

Pliszczuk³

et al. 2023

Sensors

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This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups.

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Section: Introductionmentioning

confidence: 99%