C. Pavan Kumar

Arnab Maity, Manasa, Pavan Kumar C et al.

Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10.

G. Vishnu Murthy, Pavan Kumar C., Vakulabharanam Vijaya Kumar

Study of patterns on images is recognized as an important step in characterization and classification of image. The ability to efficiently analyze and describe image patterns is thus of fundamental importance. The study of syntactic methods of describing pictures has been of interest for researchers. Array Grammars can be used to represent and recognize connected patterns. In any image the patterns are recognized using connected patterns. It is very difficult to represent all connected patterns (CP) even on a small 3 x 3 neighborhood in a pictorial way. The present paper proposes the model of array grammar capable of generating any kind of simple or complex pattern and derivation of connected pattern in an image neighborhood using the proposed grammar is discussed.