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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SK: Conceptualization, Methodology, Software, Writing - original draft, Visualization. HR: Conceptualization, Writing - review & editing. SD: Data curation, Validation. DB: Supervision, Writing - review.
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Koley, S., Roy, H., Dhar, S. et al. Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP). Pattern Anal Applic 26, 847–859 (2023). https://doi.org/10.1007/s10044-023-01139-x
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DOI: https://doi.org/10.1007/s10044-023-01139-x