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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Pattarasinee Bhattarakosol
Chulalongkorn University

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Facial action coding-based facial sub-structures for anxiety emotion classification Rawinan Praditsangthong; Pattarasinee Bhattarakosol
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp208-218

Abstract

Most stroke patients usually have problems in communication and body movement, such as speaking, sitting, walking, and picking up items. Moreover, the number of caregivers is smaller than the number of stroke patients. Nevertheless, these patients need 24-hour caregivers for the patients’ safety. Therefore, the objective of this research is to determine patterns of anxiety emotion via facial expressions from the sub-structures on the face, such as the inner brow raiser, brow lower, lid raiser, and lip part. Random samples of 360 facial images from horror-thriller movies based on the internet movie database (IMDb) website were selected. Then, 68 facial landmarks for classifying the emotions were applied to each facial image. The differences in these 68 positions before and after the changed emotions were used as the emotional indicators. Furthermore, these different values are applied to implement a decision tree with all the boundaries of the sub-structures disclosed as a suitable classification model for emotion detection. Consequently, the accuracy when applying this decision tree with other facial images is 83.33%.
Robust palmprint biometric solution for secure mobile authentication Son Nguyen; Arthorn Luangsodsai; Pattarasinee Bhattarakosol
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp680-689

Abstract

Smartphones increasingly rely on biometric authentication for access to financial and personal services, creating a need for palmprint recognition that is accurate, fast, and deployable on device. This paper proposes an end-to-end smartphone palmprint authentication framework that integrates guided mobile image capture, landmark-based region-of-interest (ROI) extraction, and compact embedding inference. A ResNet-18 teacher is first trained with self-supervised contrastive learning to reduce dependence on labeled biometric data, then distilled into a lightweight MobileNetV3 student for efficient mobile deployment. The learned embeddings support both on device verification and large-scale identification using an approximate nearest neighbor index (FAISS). Experiments on a public Kaggle palm dataset achieve 99.2% accuracy with a 0.15% equal error rate (EER). On an iPhone 13, the end-to-end pipeline runs in 87.0 ms with a 12.4 MB student model. For a 1 million-entry gallery, FAISS provides 32 ms query latency while maintaining 99.5% Recall@1. Limitations include evaluation under mostly controlled capture conditions and the absence of an explicit liveness or presentation attack detection (PAD) module; future work will address unconstrained testing and anti-spoofing integration.
Co-Authors Arthorn Luangsodsai Rawinan Praditsangthong Son Nguyen