Face recognition in dense crowd usingdeep learning approaches with IP camera

• Autorzy: Mummaneni Sobhana , Mudunuri Venkata Chaitanya Satya Ramaraju , Bommaganti Sri Veerabhadra Vikas , Kalle Bhavya Vani , Jacob Novaline , Katari Emmanuel Sanjay Raj

Identyfikatory

DOI

10.35784/iapgos.6818

Warianty tytułu

PL

Rozpoznawanie twarzy w gęstym tłumieprzy użyciu metod głębokiego uczenia z kamerą IP

• Języki publikacji: EN

Abstrakty

EN

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such contexts, a model that automatically detects and recognises people's faces is needed to improve security. The automation of face recognition brings the benefit of a more efficient and accurate solution. This paper proposes an advanced model that has the ability to detect and recognise faces in dense crowds by using deep learning techniques. Where the input is live video, the process involves splitting the video into frames and each frame is fed into the model. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) algorithm is used for face detection. It accurately locates faces in frames and images and generates boundaries around the faces as output.The detected faces are then fed as input to a model, where they are compared with data from the database. If a face is recognised, the nameof the recognised person is displayed in the boundary box of the frame, otherwise it is displayed that the person is unknown.FaceNet is used for face recognition tasks.

PL

System rozpoznawania twarzy to biometryczny system bezpieczeństwa i nadzoru, który może identyfikować i monitorować osobyw zatłoczonym obszarze. Ręczne monitorowanie zatłoczonego środowiska jest trudnym i podatnym na błędy zadaniem. Dlatego w takich okolicznościach, aby poprawić bezpieczeństwo, potrzebny jest model, któryautomatycznie wykrywa i rozpoznaje twarze osób. Automatyzacja rozpoznawania twarzy przynosi korzyści w postaci bardziej wydajnego i dokładnego rozwiązania. W niniejszym artykule zaproponowano zaawansowany model, który ma zdolność wykrywania i rozpoznawania twarzy w gęstym tłumie dzięki zastosowaniu technik głębokiego uczenia. W przypadku gdy danymi wejściowymi jest wideo na żywo, proces obejmuje dzielenie wideo na klatki, a każda klatka jest podawana do modelu. Algorytm Multi-Task Cascaded Convolutional Neural Networks (MTCNN) jest używany do wykrywania twarzy. Dokładnie lokalizuje twarze w klatkach i obrazach oraz generuje obwiedniewokół twarzy jako dane wyjściowe. Następnie wykryte twarze są podawane jako dane wejściowe do modelu, w którym są porównywanez danymi z bazy danych. W przypadku rozpoznania twarzy w polu granicznym ramki jest wyświetlane imię rozpoznanej osoby, w przeciwnym razie jest wyświetlana informacja, że osoba jest nieznana. FaceNet jest używany do zadań rozpoznawania twarzy.

Słowa kluczowe

EN

face detection; multi-task cascaded convolutional neural networks; dense crowd; FaceNet

PL

wykrywanie twarzy; wielozadaniowe kaskadowe konwolucyjne sieci neuronowe; gęsty tłum; FaceNe

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2025
• Tom: T. 15, nr 2
• Strony: 44--50
• Opis fizyczny: Bibliogr. 27 poz., fot.

Twórcy

autor

Mummaneni Sobhana

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering, Vijayawada, India

• https://orcid.org/0000-0001-5938-5740+

autor

Mudunuri Venkata Chaitanya Satya Ramaraju

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Artificial Intelligence and Data Science, Vijayawada, India

• https://orcid.org/0009-0007-9011-3170+

autor

Bommaganti Sri Veerabhadra Vikas

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Artificial Intelligence and Data Science, Vijayawada, India

• https://orcid.org/0009-0008-3025-8690+

autor

Kalle Bhavya Vani

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Artificial Intelligence and Data Science, Vijayawada, India

• https://orcid.org/0009-0008-1273-1480+

autor

Jacob Novaline

• Advanced Data Processing Research Institute, Departmentof Space, Hyderabad, India

• https://orcid.org/0009-0007-8627-9231+

autor

Katari Emmanuel Sanjay Raj

• Advanced Data Processing Research Institute, Departmentof Space, Hyderabad, India

• https://orcid.org/0009-0003-6389-6625+

Bibliografia

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