Ali, Hasimah and Kang, Ho Yong and Ahmad, Wan Khairunizam Wan and Elshaikh, Mohamed and Mokhtar, Norrima (2022) Classification of body mass index based face images using facial landmarks approach and PCA plus LDA. In: 27th International Conference on Artificial Life and Robotics, ICAROB 2022, 20-23 January 2022, Virtual, Online.
Full text not available from this repository.Abstract
Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding “facial information” from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) using facial landmark approach based on facial images. In this framework, Discriminative Response Map Fitting (DRMF) method has been used as feature extraction technique to detect and locate the facial landmark points on the facial images. About sixty-six (66) facial landmark points were identified. Only nineteen (19) of facial landmark points have been employed to extract the facial features in terms of distance and ratio features. A total of 221 facial landmark features were obtained and used as feature vector to classify the BMI classes. The rationale of using 221 facial landmark features is because these features were able to exhibit the unique characteristic of the BMI classes, which are normal, overweight and obese. Then, the extracted features were further reduced using Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) to map high dimension features into low dimensional feature with maximize between class scatter and minimize within class variations. Later, the reduced features were subjected to k-NN classifiers. A series of experiments has been conducted on MORPH II database using the reduced facial landmark features to classify the three BMI classes. Based on the experimental results, it shows that the reduced features using PCA plus LDA based on k-NN classifier has achieve the highest recognition rate with accuracy of 83.33 . The obtained results show that the reduced facial landmark features were able to discriminate the three BMI classes of normal, overweight and obese, thus shows the promising results. © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022).
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Body mass index; Facial images; Facial landmarks; PCA plus LDA |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Feb 2025 07:28 |
| Last Modified: | 24 Feb 2025 07:28 |
| URI: | http://eprints.um.edu.my/id/eprint/43266 |
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