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Pattern Analysis and Applications

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17.02.2023 | Short Paper

Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)

verfasst von: Subhadeep Koley, Hiranmoy Roy, Soumyadip Dhar, Debotosh Bhattacharjee

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2023

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Abstract

With the ever-increasing security threats in recent years, biometric authentication has become omnipresent. Among all biometric characteristics, face recognition research has gained traction lately. This paper proposes a new face image descriptor named Local Discrete Cosine Transform Binary Pattern (LDCTBP) for illumination- and modality-invariant face recognition. Utilizing the frequency segregation behavior of Discrete Cosine Transform (DCT), an effective cross-modal illumination-agnostic local feature descriptor has been formulated. Eventually, by encoding the illumination-normalized DCT coefficients into a binary pattern, Local Discrete Cosine Transform Binary Pattern has been generated. Qualitative and quantitative analysis performed on the Extended Yale-B, CUFSF, and TUFTS dataset depict the supremacy of the proposed framework over other state-of-the-arts. Moreover, the proposed LDCTBP has been integrated with a light-weight Convolutional Neural Network (CNN) to prove the importance of handcrafted features in CNN training.

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Vorheriger Artikel A review of natural language processing in contact centre automation

Nächster Artikel Deep neural network watermarking based on a reversible image hiding network

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Titel: Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)
verfasst von: Subhadeep Koley
Hiranmoy Roy
Soumyadip Dhar
Debotosh Bhattacharjee
Publikationsdatum: 17.02.2023
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-023-01139-x

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Abstract

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