Biometric applications, such as person re-identification (ReID), are often deployed on energy constrained devices. While recent ReID methods prioritize high retrieval performance, they often come with large computational costs and high search time, rendering them less practical in real-world settings. In this work, we propose an input-adaptive network with multiple exit blocks, that can terminate computation early if the retrieval is straightforward or noisy, saving a lot of computation. To assess the complexity of the input, we introduce a temporal-based classifier driven by a new training strategy. Furthermore, we adopt a binary hash code generation approach instead of relying on continuous-valued features, which significantly improves the search process by a factor of 20. To ensure similarity preservation, we utilize a new ranking regularizer that bridges the gap between continuous and binary features. Extensive analysis of our proposed method is conducted on three datasets: Market1501, MSMT17 (Multi-Scene Multi-Time), and the BGC1 (BRIAR Government Collection). Using our approach, more than 70% of the samples with compact hash codes exit early on the Market1501 dataset, saving 80% of the networks computational cost and improving over other hash-based methods by 60%. These results demonstrate a significant improvement over dynamic networks and showcase comparable accuracy performance to conventional ReID methods. Code will be made available.

翻译：生物识别应用，如行人重识别（ReID），通常部署在能量受限的设备上。尽管近年来的ReID方法优先考虑高检索性能，但往往伴随着巨大的计算成本和高搜索时间，使其在现实场景中实用性降低。在这项工作中，我们提出了一种具有多个退出模块的输入自适应网络，当检索简单或存在噪声时可提前终止计算，从而节省大量计算资源。为了评估输入的复杂度，我们引入了一种基于时间序列的分类器，并由新的训练策略驱动。此外，我们采用二元哈希码生成方法替代依赖连续值特征的方式，将搜索过程效率显著提升20倍。为确保相似性保持，我们利用了一种新的排序正则化器，弥合了连续特征与二元特征之间的差距。我们在三个数据集上对所提方法进行了广泛分析：Market1501、MSMT17（多场景多时相）和BGC1（BRIAR政府收集数据集）。采用我们的方法，在Market1501数据集中，超过70%的紧凑哈希码样本实现了提前退出，节省了80%的网络计算成本，并比其他基于哈希的方法提升了60%。这些结果表明，相较于动态网络有显著改进，且准确率性能与传统ReID方法相当。代码将开源。