ISSN :2582-9793

Investigating the Impact of Yaw Pose Variation on Facial Recognition Performance

Original Research (Published On: 29-May-2023 )
Investigating the Impact of Yaw Pose Variation on Facial Recognition Performance
DOI : 10.54364/AAIML.2023.1162

Omer Abdulhaleem Naser and Sharifah Mumtazah Syed Ahmad

Adv. Artif. Intell. Mach. Learn., 3 (2):1039-1055

Omer Abdulhaleem Naser : A final-year Ph.D. student at the University Putra Malaysia, Faculty of Engineering. I majored in computational methods in engineering. The specified research field is biometrics, particularly facial recognition for occluded faces.

Sharifah Mumtazah Syed Ahmad : An associate professor who currently works as a senior lecturer and supervisor at University Putra Malaysia, faculty of Engineering.

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DOI: 10.54364/AAIML.2023.1162

Article History: Received on: 18-Apr-23, Accepted on: 12-May-23, Published on: 29-May-23

Corresponding Author: Omer Abdulhaleem Naser

Email: omar.abdulhalem592@gmail.com

Citation: Omer Abdulhaleem Naser, Sharifah Mumtazah Syed Ahmad, Khairulmizam Samsudin, Marsyita Hanafi (2023). Investigating the Impact of Yaw Pose Variation on Facial Recognition Performance. Adv. Artif. Intell. Mach. Learn., 3 (2 ):1039-1055


Abstract

    

Facial recognition systems often struggle with detecting faces in poses that deviate from the frontal view. Therefore, this paper investigates the impact of variations in yaw poses on the accuracy of facial recognition systems and presents a robust approach optimized to detect faces with pose variations ranging from 0° to ±90°. The proposed system integrates MTCNN, FaceNet, and SVC, and is trained and evaluated on the Taiwan dataset, which includes face images with diverse yaw poses. The training dataset consists of 89 subjects, with approximately 70 images per subject, and the testing dataset consists of 49 subjects, each with approximately 5 images. Our system achieved a training accuracy of 99.174% and a test accuracy of 96.970%, demonstrating its efficiency in detecting faces with pose variations. These findings suggest that the proposed approach can be a valuable tool in improving facial recognition accuracy in real-world scenarios.

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