HashReID: Dynamic Network with Binary Codes for Efficient Person
  Re-identification
Kshitij Nikhal Yujunrong Ma Shuvra S. Bhattacharyya Benjamin S. Riggan
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.