Large group face recognition for attendance tracking

Large group facial recognition for attendance taking has a very practical application use case in today’s academic institutions. With the multitude of modern state-of-the-art models being developed, this project will serve to evaluate the feasibility of large group facial recognition for attendance...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Yeap, Jia Hao
مؤلفون آخرون: Ong Chin Ann
التنسيق: Final Year Project
اللغة:English
منشور في: Nanyang Technological University 2024
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/175039
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:Large group facial recognition for attendance taking has a very practical application use case in today’s academic institutions. With the multitude of modern state-of-the-art models being developed, this project will serve to evaluate the feasibility of large group facial recognition for attendance taking, using selected modern models. Three experiments conducted on the custom face detection dataset, LFW and CPLFW respectively, showed that RetinaFace was the best facial detection model while ArcFace with Cosine similarity and FaceNet512 with Euclidean L2 similarity were the best facial recognition models. Additionally, this project uncovered upscaling and ensemble embeddings as novel techniques that led to significant improvements in model performance. Using the best models and novel techniques analysed in the experiments, a prototype was developed. Evaluating the prototype on the custom evaluation dataset highlighted the limitations of current models and methods. The report thus concludes that large group facial recognition is still not feasible for practical uses as the prototype’s recognition accuracies, while decent, is still relatively far from the ideal accuracy of 100%.