Electronics, Vol. 14, Pages 2093: Left Meets Right: A Siamese Network Approach to Cross-Palmprint Biometric Recognition

Electronics doi: 10.3390/electronics14102093

Authors:
Ezz

What if you could identify someone’s right palmprint just by looking at their left—and vice versa? That is exactly what I set out to do. I built a specially adapted Siamese network that only needs one palm to reliably recognize the other, making biometric systems far more flexible in everyday settings. My solution rests on two simple but powerful ideas. First, Anchor Embedding through Feature Aggregation (AnchorEFA) creates a “super‑anchor” by averaging four palmprint samples from the same person. This pooled anchor smooths out noise and highlights the consistent patterns shared between left and right palms. Second, I use a Concatenated Similarity Measurement—combining Euclidean distance with Element‑wise Absolute Difference (EAD)—so the model can pick up both big structural similarities and tiny textural differences. I tested this approach on three public datasets (POLYU_Left_Right, TongjiS1_Left_Right, and CASIA_Left_Right) and saw a clear jump in accuracy compared to traditional methods. In fact, my four‑sample AnchorEFA plus hybrid similarity metric did not just beat the baseline—it set a new benchmark for cross‑palmprint recognition. In short, recognizing a palmprint from its opposite pair is not just feasible—it is practical, accurate, and ready for real‑world use. This work opens the door to more secure, user‑friendly biometric systems that still work even when only one palmprint is available.

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