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All Journal Techno.Com: Jurnal Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI bit-Tech Jurnal Ilmiah Teknologi Informasi dan Robotika Journal of Informatics and Computer Science (JINACS) Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
SALAMUN ROHMAN NUDIN
Universitas Negeri Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : bit-Tech

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Personality Prediction Based on Video Using Transfer Learning DeepID Model Pradana, Handika Dio; Nudin, Salamun Rohman
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2866

Abstract

This research presents an automatic personality prediction system based on the Big Five model openness, conscientiousness, extraversion, agreeableness, and neuroticism by leveraging transfer learning on the DeepID architecture. Video input is first processed with the MTCNN algorithm for robust facial region detection under varying lighting and poses. Extracted features are fed into a modified DeepID model, pre-trained on large-scale face-recognition datasets, to perform spatial encoding. To capture temporal dynamics, Long Short-Term Memory (LSTM) networks model frame-to-frame changes in expression. Training and validation use the ChaLearn LAP dataset of approximately 10,000 annotated videos. Experimental results demonstrate 88.6% overall accuracy, with an average precision of 87.2%, recall of 86.5%, and F1-score of 86.8%, confirming the model’s balanced performance across classes. A minimum loss of 11.3% further underscores effective convergence. The complete pipeline is deployed via Flask, enabling real-time, web-based integration. Beyond academic novelty, this system holds promise for practical applications: in recruitment, it can offer unbiased, rapid personality screening; in mental-health contexts, it may assist clinicians by flagging behavioral cues non-invasively; and in human–computer interaction, adaptive interfaces could personalize responses based on users’ inferred traits. By combining transfer learning with temporal modeling, our approach delivers a scalable, non-invasive tool for automated psychological assessment through visual data, paving the way for ethical, real-time personality analytics in diverse domains.
Co-Authors Abdul Khamid Abdul Khamid Alamsyah, Shandy Ilham Alen Bachtiar Heriyanto Anggraeni, Erika Kanya Arifin, Achmad Nurs Syururi Asyadana, Aldi Naufal Augusta Jannatul Firdaus, Reza Celia Angelina Nogo Koban Chuluq, Khusnul Elok Nur Fauziyah Enggarbela Ogi Intan Pratiwi Enggarbela Ogi Intan Pratiwi Fitria Hidayanti Fitria Hidayanti Ghoffar, Alghifar Abdul Ghoffar, Alghifar Abdul Laili Indah Liana Natanael Chrisanta Rikandi Panji Sulanggalih, Mochammad Pradana, Handika Dio Prawira, Indra Waskita Prismana, I Gusti Lanang Putra Eka Qholisa, Siti Nur Rama Syaiful Arif Rifky Fajar Oktanugraha Shinta Yuan Ayu Pratiwi Sugiarto - SUGIARTO - Yisti Vita Via Yisti Vita Via