Intrinsic Property-Based Soft Biometric Classification Using Wavelet CNNs and LRP

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Abdullahi S.B.; Chamnongthai K.

ผู้เผยแพร่: Society of Photo-optical Instrumentation Engineers

ปีที่เผยแพร่ (ค.ศ.): 2025

วารสาร: Proceedings of SPIE (0277-786X)

Volume number: 13518

ISBN: 978-151068829-2

นอก: 0277-786X

eISSN: 1996-756X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219568156&doi=10.1117%2f12.3058542&partnerID=40&md5=36623ab2ea94571fc0a80bd52679ea92

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

The rapid evolution of optical sensors has significantly impacted the field of soft biometrics, necessitating the development of adaptive automatic deep learning methods. Traditional deep-learning approaches for feature extraction and classification often struggle with biases related to sensor position and activity configuration. Recent advancements in deep learning have leveraged attribute representation-based transfer learning for effective biometrics identification. However, these methods are limited in identifying the most contributive features for classification tasks. This study proposes a novel approach to transform human activity features using the effective wavelet transform (eW), which minimizes sensor biases by converting activity features into a novel representation of intrinsic biometric properties. These features are then learned through a well-trained Wavelet Convolutional Neural Network (eWCNN). To interpret the eWCNN, we employ Layer-wise Relevance Propagation (LRP) to analyze the features and plot relevance scores. Our method outperforms existing state-of-the-art techniques, providing an interpretable solution for soft biometrics identification and classification. © 2025 SPIE.

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