ATM Security System Modeling Using Face Recognition with FaceNet and Haar Cascade

Ferdinand, J.M.; Wijaya, Cindy; Ronal, Andreas Noel; Edbert, Ivan Sebastian; Suhartono, Derwin

doi:10.1109/icicos56336.2022.9930563

Cited by 5 publications

(2 citation statements)

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“…Ronal, I.S. Edbert, and D. Suhartono et al [4] proposed a face Table 1. Input parameters Input Name Pin Email id Real-Time Image Capture recognition system using FaceNet combined with the Haar Cascade Classi ier.…”

Section: Literature Surveymentioning

confidence: 99%

Unlocking the Future of Secure Automatic Machines: Leveraging FaceReg with HRC & LBPH

Vijaywargiya,

Mishra,

Doohan

2024

JAMRIS

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We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features, like PIN and face recognition, help to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user turns out to be unauthorized also.It does not allow them to log in into the machine, which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.

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Section: Literature Surveymentioning

confidence: 99%

Unlocking the Future of Secure Automatic Machines: Leveraging FaceReg with HRC & LBPH

Vijaywargiya,

Mishra,

Doohan

2024

JAMRIS

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“…Once we have the hyperplane, we can use it as input into a classifier to determine the "predicted" class [7]. This paper provides an overview of recent facial recognition approaches that are currently available in section 2 [8]- [17]. In addition, an overview of popular facial recognition techniques and their general classifications are provided.…”

Section: Introductionmentioning

confidence: 99%

Facial recognition for partially occluded faces

Naser

Ahmad

Samsudin

et al. 2023

IJEECS

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Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.

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