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Journal : JAREE (Journal on Advanced Research in Electrical Engineering)

Multiple Face Tracking using Kalman and Hungarian Algorithm to Reduce Face Recognition Computational Cost Willy Achmat Fauzi; Supeno M Susiki Nugroho; Eko Mulyanto Yuniarno; Wiwik Anggraeni; Mauridhi Hery Purnomo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 1 (2021): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i1.191

Abstract

Currently, research in face recognition systems mainly utilized deep learning to achieve high accuracy. Using deep learning as the base platform, per frame image processing to detect and recognize faces is computationally expensive, especially for video surveillance systems using large numbers of mounted cameras simultaneously streaming video data to the system. The idea behind this research is that the system does not need to recognize every occurrence of faces in every frame. We used MobileNet SSD to detect the face, Kalman filter to predict face location in the next frame when detection fails, and Hungarian algorithm to maintain the identity of each face. Based on the result, using our algorithm 87.832 face that must be recognized is reduced to only 204 faces, and run at the real-time scenario. This method is proven to be used in surveillance systems by reducing the computational cost.Keywords: Hungarian algorithm, Kalman filter, multiple face tracking, video surveillance system.
Facial Movement Recognition Using CNN-BiLSTM in Vowel for Bahasa Indonesia Rahman, Muhammad Daffa Abiyyu; Wicaksono, Alif Aditya; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 1 (2024): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i1.372

Abstract

Speaking is a multimodal phenomenon that has both verbal and non-verbal cues. One of the non-verbal cues in speaking is the facial movement of the subject, which can be used to find the letter being spoken by the subject. Previous research has been done to prove that lip movement can translate to vowels for Bahasa Indonesia, but detecting the whole facial movement is yet to be covered. This research aimed to establish a CNN-BiLSTM model that can learn spoken vowels by reading the subject's facial movements. The CNN-BiLSTM model yielded a 98.66% validation accuracy, with over 94% accuracy for all five vowels. The model is also capable of recognizing whether the subject is currently silent or speaking a vowel with 98.07% accuracy.
Markerless Facial Reconstruction Motion Capture Using Triangulation Method Alwali, Muhammad; Pambudi, Sevito Fernanda; Suciningtyas, Laras; Yuniarno, Eko Mulyanto
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 9, No 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.456

Abstract

Motion capture is a popular research topic, with one of its main applications being human face reconstruction. The demand for converting 2D images into 3D reconstructions continues to increase, especially in facial reconstruction, where progress is made in improving the accuracy of facial position prediction. However, there is still a significant gap in developing facial reconstruction technologies that can consistently convert 2D to 3D data with high accuracy, especially in scenarios involving dynamic facial expressions, diverse facial angles, and complex environmental conditions. Therefore, an approach using the triangulation method for 3D face reconstruction in the real world was developed. In the experiments, two cameras were used to obtain two face landmark coordinates so that the triangulation method can be implemented for 3D face reconstruction. This research aims to develop a motion capture approach that is able to accurately and efficiently transform 2D data into 3D face models without the need for complex hardware. The main contribution of this research is the development of a machine learning-based markerless motion capture technique designed to improve the accuracy of face position prediction in 3D face reconstruction from 2D data in realistic environments. This method seeks to bridge the current technology gap by providing a more flexible and reliable solution, expanding the potential applications of motion capture in various fields without dependence on specialized hardware. The results of face reconstruction research using markerless motion capture and triangulation method show RMSE values of 3.560839 for eyes, 1.644749 for nose, and 4.054638 for lips.
Co-Authors Aditya Nur Ikhsan Soewidiatmaka Agung Dewa Bagus Soetiono Agung Wicaksono Agustinus Bimo Gumelar Ahmad Zaini Alan Luthfi Ali Sofyan Kholimi Alwali, Muhammad Anang Kukuh Adisusilo Andreas Agung Kristanto, Andreas Agung Ardyono Priyadi Arief Kurniawan Arik Kurniawati Aris Widayati Atyantagratia Vidyasmara Daryanto Bambang Purwantana Beny Yulkurniawan Victorio Nasution Beny Yulkurniawan Victorio Nasution Boedinoegroho, Hanny Citra Ratih Prameswari Diah Puspito Wulandari Endang Setyati Endang Sri Rahayu Enggartiasto Faudi Ristyawan Esther Irawati Setiawan Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fakih, Muhammad Fadli Fandiantoro, Dion Hayu Farah Zakiyah Rahmanti Farodisa, Annida Miftakhul Feby Artwodini Muqtadiroh Fresy Nugroho Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Goenawan A Sambodo Gunawan Gunawan Gunawan Hardianto Wibowo Harfianti, Nadya Putri Herman Thuan Herman Thuan To Saurik Hermawan, Norma Hervit Ananta Vidada Hutama Harsono, Nathanael I Ketut Eddy Purnama I Made Gede Sunarya Imam Robandi Indar Sugiarto Isa Hafidz Ismoyo Sunu Jaya Pranata Joan Santoso Joko Priambodo Khairunnas Khairunnas Koeshardianto, Meidya Kurniawan, Arief Lailatul Husniah Lutfi Ananditya Septiandi Masy Ari Ulinuha Matahari Bhakti Nendya Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Moch. Iskandar Riansyah Mochamad Hariadi Mochamad Yusuf Alsagaff Muhammad Fadli Fakih Muhammad Reza Pahlawan Muhammad Zulfikar Alfathan Rachmatullah Muhtadin Mulyanto, Edy Myrtati Dyah Artaria Nasrulloh, Muhammad Nova Eka Budiyanta Nugroho, Vidityar Adith Oddy Virgantara Putra Pambudi, Sevito Fernanda Pramunanto, Eko Pramunanto, Eko Priambodo, Joko Putu Hendra Suputra R Dimas Adityo Radi Radi Rafly Azmi Ulya, Amik Ragil Bintang Brilyan Rahman, Muhammad Daffa Abiyyu Reza Fuad Rachmadi Rika Rokhana Rika Rokhana Riris Diana Rachmayanti Rokhana, Rika S. Suprapto Saiful Yahya Sambodo, Goenawan A Samuel Gandang Gunanto Sensusiati, Anggraini Dwi Setiawan, Rachmad Setijadi, Eko Soetiono, Agung Dewa Bagus Suciningtyas, Laras Sugiyanto - Sulistyono, Marcelinus Yosep Teguh Supeno M Susiki Nugroho Supeno Mardi Susiki Supeno Mardi Susiki Supeno Mardi Susiki N Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Surya Sumpeno Susiki N, Supeno Mardi Syauqi Sabili Tita Karlita Tita Karlita Tita Karlita Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Willy Achmat Fauzi Wisnu Widiarto Wiwik Anggraeni Yose Rizal Yose Rizal Yoyon K. Suprapto Yoyon K. Suprapto Yoyon Kusnendar Suprapto Yuhana, Umi Laili Zaini, Ahmad