Real- Time Attendance System Using Face Recognition Technique

Ghosh, Debadrita

doi:10.33564/ijeast.2021.v05i09.043

Cited by 2 publications

(1 citation statement)

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“…In summary, triplet loss minimizes the distance between anchors and positives with the same identity and maximizes the distance between anchors and negatives with different identities. For more information on FaceNet, see the study "FaceNet: Integrated Embedding for Face Recognition and Clustering" [15] Table 2 The primary purpose of feature extraction is to reduce machine training time and space complexity in order to reduce dimension [8]. The proposed system's feature extraction and face representation are based on the work of [18].…”

Section: Figure 5 the Triplet Lossmentioning

confidence: 99%

A deep learning-based face recognition attendance system

Ubong David Essien,

Godwin Okon Ansa

2023

Global J. Eng. Technol. Adv.

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Deep learning-based face recognition system have produced high accuracy and better performance when compared to other methods of face recognition like the eigen faces. Modern face recognition systems consist of different phases such as face detection, face alignment, feature extraction, face representation and face recognition. This paper proposes a deep learning approach in developing a face recognition-based class attendance system. The Multitask Convolutional Neural Network (MTCNN) is used for the face detection and alignment phase and a lightweight hybrid high performance Deepface Python framework based on the ‘Deepface’ Deep Convolutional Neural Network is employed for the feature extraction, face representation and face recognition phases with FaceNet-512 pretrained model. Because Convolutional Neural Networks (CNNs) perform better with larger datasets, image augmentation will be used on the original photos to enlarge the tiny dataset. The attendance record is stored in a MySQL database and accessed by an Application Programming Interface (API) developed using Hypertext Pre-processor (PHP) CodeIgniter framework. Cosine Similarity is used as the similarity metrics to compare the facial embeddings. A sliding camera system is deployed to aids the full coverage of the class participants irrespective of the size of the class. The test result show that all class participants were correctly identified and captured in the class attendance register generated.

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Section: Figure 5 the Triplet Lossmentioning

confidence: 99%