Challenges in gait recognition arise from the difficulty of subject identification through subtle spatial differences. Learning the temporal representation helps, however, straightforward temporal modeling like 3D Convolution not only incurs excessive computational complexity, but may also further interfere with the already limited spatial details. In this paper, we present a Spatial-Temporal Decoupled Network (STDN) to balance the trade-off between the cross-frame interference, computational complexity, and model performance. Specifically, motivated by the concept of spatial-temporal decoupling, we propose a Decoupled Parallel Feature Extractor (DPFE) for noise-suppressed spatial-temporal features extraction. By incorporating DPFE as the basic component, we build a Efficient Spatial-temporal Awareness Block (ESA Block) that accounts for the unique properties of gait data, namely, restricted spatial information and abundant temporal information, to minimize the computational overhead imposed by temporal modeling. Experiments on CASIA-B and OU-MVLP datasets demonstrate the state-of-the-art performance of our proposed method, with an average rank-1 accuracy of 93.7% on the CASIA-B and 90.5% on the OU-MVLP. The source code is available on https://github.com/tk59854/stdn.

Low‐light enhancement methods suffer from the over‐enhancement problem which could induce the loss of the important texture and make images look unnatural. Moreover, some low‐light images contain strong light areas that must be weakened to improve the visual effect. In this study, an enhancement method with strong light weakening and bright halo suppressing is presented. Firstly, the bright channel prior is applied to the inverted image to weaken the strong light both in and around the strong light areas. Then, a dehazing‐type of algorithm with the dark channel prior is employed via superpixel segmentation to enhance the low‐light image. Finally, a revised non‐local denoising method is proposed to further refine the enhanced image. Experimental results showed that the proposed method achieved better visual effects compared with other state‐of‐the‐art methods. Besides, the quantitative evaluation showed that the authors’ method outperforms the other methods both in the aspect of enhancement and denoising.

Recent technological advances have allowed for a proliferation of digital evidence images. Using these images as evidence in legal cases (e.g. child sexual abuse, child pornography and masked gunmen) can be very challenging, because the faces of criminals or victims are not visible. Although large skin marks and tattoos have been used, they are ineffective in some legal cases, because the skin exposed in evidence images neither have unique tattoos nor enough skin marks for identification. The blood vessel between the skin and the muscle covering most parts of the human body is a powerful biometric trait, because of its universality, permanence and distinctiveness. Traditionally, it was impossible to use vein patterns for forensic identification, because they were not visible in color images. This paper proposes an algorithm to uncover vein patterns from the skin exposed in color images for personal identification. Based on the principles of optics and skin biophysics, we modeled the inverse process of skin color formation in an image and derived spatial distributions of biophysical parameters from color images, where vein patterns can be observed. Experimental results are very encouraging. The clarity of the vein patterns in resultant images is comparable to or even better than that in near infrared images.

Identifying criminals and victims in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used nevi to identify a pedophile in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affected the visibility of the nevi. In addition to this case, a huge amount of JPEG compressed child pornography is posted on-line every day. Although many methods have been proposed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based algorithm, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training dataset which contains original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer original blocks of compressed evidence images. An indexing mechanism is also proposed to deal with large datasets efficiently. Extensive experiments are conducted on images with different characteristics and compression ratios. Both visual comparison and subjective evaluation demonstrate that the proposed algorithm is more effective than other methods.

Identifying individuals in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used pigmented skin marks to identify a suspect in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affect the visibility of the small skin marks. In addition to this case, a huge amount of JPEG-compressed child pornography is posted online every day. Although many methods have been developed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based (KB) approach, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training set containing both original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer the original blocks of compressed evidence images. A Markov-model-based algorithm and a faster one-pass algorithm were developed to make inference, and a block synthesis algorithm was developed to handle the cases where compressed blocks are not contained in the training set. An indexing mechanism was also proposed to deal with large datasets efficiently. Extensive experiments were conducted on images with different characteristics and compression ratios. Both subjective and objective evaluations demonstrated that the KB approach is more effective than other methods. In summary, the KB approach is capable of removing blocking artifacts to recover useful skin features.