Kernel-Based Semantic Hashing for Gait Retrieval

It is very important to retrieve a specific person in locating and tracking the missing people as well as the suspects quickly. However, the well-studied face-based and appearance-based individual retrieval methods are ineffective in the surveillance scenarios because of the far photograph distances, the low camera resolutions, the long time intervals, and the complex lighting conditions. To avoid the disadvantages of face-based and appearance-based methods, we propose to retrieve individuals from the surveillance videos with the gait biometric, which has been proved to be beneficial to remote person recognition and robust to lighting variations. What's more, the gait biometric can be collected without conscious cooperation, making the data collection much easier. But it varies greatly with the view angles, the clothing style, and the carrying conditions. Therefore, the videos of the target person from a similar view angle with the same clothing style and carrying conditions should rank higher than the others. To achieve this purpose and improve the efficiency, this paper proposes a kernel-based semantic hashing (KSH) model, which is learnt by optimizing a semantic triplet ranking loss. Specifically, in the training phase, a semantic similarity score, which depends on the view angles, the clothing style, and the carrying conditions, is calculated for each training pair. Then, a weighted triplet loss considering these semantic scores is designed, which encourages videos with a higher score to stay closer to the gallery in the binary Hamming space. To evaluate the performance of the proposed method, we compare it with several methods on the CASIA Gait Database B and the OU-ISIR Gait Database. The experimental results demonstrate that the KSH is effective and efficient.