  Palmprint biometrics garner heightened attention in palm-scanning payment and
social security due to their distinctive attributes. However, prevailing
methodologies singularly prioritize texture orientation, neglecting the
significant texture scale dimension. We design an innovative network for
concurrently extracting intra-scale and inter-scale features to redress this
limitation. This paper proposes a scale-aware competitive network (SAC-Net),
which includes the Inner-Scale Competition Module (ISCM) and the Across-Scale
Competition Module (ASCM) to capture texture characteristics related to
orientation and scale. ISCM efficiently integrates learnable Gabor filters and
a self-attention mechanism to extract rich orientation data and discern
textures with long-range discriminative properties. Subsequently, ASCM
leverages a competitive strategy across various scales to effectively
encapsulate the competitive texture scale elements. By synergizing ISCM and
ASCM, our method adeptly characterizes palmprint features. Rigorous
experimentation across three benchmark datasets unequivocally demonstrates our
proposed approach's exceptional recognition performance and resilience relative
to state-of-the-art alternatives.
