Recently, depression recognition has received considerable attention. Due to easy acquisition at a distance, gait-based depression recognition can be a useful tool for auxiliary diagnosis and ...self-help depression risk assessment. Most existing methods use a few hand-craft features for analysis. To further investigate the relationship between depression and gait, we collect a gait-based depression dataset named D-Gait. 27,120 gait sequences of 292 volunteers with informed consent are collected to support the data-driven methodology (the private information is eliminated). Multiple shooting angles and three kinds of clothing are taken into consideration. To the best of our knowledge, it is the first published gait-based depression dataset. Based on this dataset, a gait-based depression risk recognition benchmark is established. We systematically investigate representative methods with skeleton and silhouette data, thereby verifying most psychological research conclusions about the relationship between gait and depression. Besides, more instructive insights are given in our paper, which indicates the significant potential of gait-based depression risk recognition. The benchmark will advance the research on depression, and it can be accessed at https://github.com/BNU-IVC/D-Gait.
Human action recognition (HAR) has gained much attention in the last few years due to its enormous applications including human activity monitoring, robotics, visual surveillance, to name but a few. ...Most of the previously proposed HAR systems have focused on using hand-crafted images features. However, these features cover limited aspects of the problem and show performance degradation on a large and complex datasets. Therefore, in this work, we propose a novel HAR system which is based on the fusion of conventional hand-crafted features using histogram of oriented gradients (HoG) and deep features. Initially, human silhouette is extracted with the help of saliency-based method - implemented in two phases. In the first phase, motion and geometric features are extracted from the selected channel, whilst, second phase calculates the Chi-square distance between the extracted and threshold-based minimum distance features. Afterwards, extracted deep CNN and hand-crafted features are fused to generate a resultant vector. Moreover, to cope with the curse of dimensionality, an entropy-based feature selection technique is also proposed to identify the most discriminant features for classification using multi-class support vector machine (M-SVM). All the simulations are performed on five publicly available benchmark datasets including Weizmann, UCF11 (YouTube), UCF Sports, IXMAS, and UT-Interaction. A comparative evaluation is also presented to show that our proposed model achieves superior performances in comparison to a few exiting methods.
•Motion and Geometric features are extracted for human flow estimation and silhouette extraction.•Deep CNN and hand crafted features are fused through parallel approach.•Entropy-controlled Chi-square approach is proposed for best features selection.•Experiments are performed on several well-known datasets.
•A comprehensive survey of biometric gait recognition based on vision, underfoot pressure, accelerometry, and audio sensory modalities.•A review of the factors that impact gait recognition ...performance (e.g., walking speed, clothing, footwear, etc.) and the influence of time lapse.•A discussion on the future of gait biometrics and the challenges and open problems that are yet to be addressed in the field.
The scientific literature on automated gait analysis for human recognition has grown dramatically over the past 15 years. A number of sensing modalities including those based on vision, sound, pressure, and accelerometry have been used to capture gait information. For each of these modalities, a number of methods have been developed to extract and compare human gait information, resulting in different sets of features. This paper provides an extensive overview of the various types of features that have been utilized for each sensing modality and their relationship to the appearance and biomechanics of gait. The features considered in this work include (a) static and dynamic (temporal) features; (b) model-based and model-free visual features; (c) ground reaction force-based and finely resolved underfoot pressure features; (d) wearable sensor features; and (e) acoustic features. We also review the factors that impact gait recognition, and discuss recent work on gait spoofing and obfuscation. Finally, we enumerate the challenges and open problems in the field of gait recognition.
As an important branch of emerging artificial intelligence algorithms, multi-agent reinforcement learning (MARL) has shown strong performance in collaborative environments. It can utilize multiple ...agents to find the optimal set of strategies for solving sequential decision problem through trial-and-error. One of the main challenges facing multi-agent system is the non-stationarity problem, which brings poor convergence and seriously affects its performance. Clustering is a commonly used unsupervised analytical method in machine learning, which aims to group samples with similar internal properties into the same cluster. In this paper, we propose a MARL clustering algorithm based on silhouette coefficient (SC-MARLC), and use the trial-and-error strategy to find the best cluster groups. In SC-MARLC, we establish a mapping relationship between multi-agent and samples, construct a novel clustering model based on MARL, and design a good clustering subset structure based on the sample silhouette coefficient. The designed structure is helpful for multi-agent system to solve the non-stationary problem. Finally, we compare the performance of SC-MARLC with 11 existing clustering algorithms on fifteen public datasets. The results show that the new clustering algorithm performs best on ten datasets.
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We introduce generalized Darboux frames along a null Cartan curve lying on a timelike surface in Minkowski space
𝔼13 and define null Cartan normal isophotic and normal silhouette curves in terms of ...the vector field that lies in the normal plane of the curve and belongs to its generalized Darboux frame of the first kind. We investigate null Cartan normal isophotic and normal silhouette curves with constant geodesic curvature
kg$$ {k}_g $$ and constant geodesic torsion
τg$$ {\tau}_g $$. We obtain the parameter equations of their axes and prove that such curves are the null Cartan helices or the null Cartan cubics. In particular, we show that null Cartan normal isophotic curves with a non‐zero constant curvatures
kg$$ {k}_g $$ and
τg$$ {\tau}_g $$ have a remarkable property that they are general helices, relatively normal‐slant helices and isophotic curves with respect to the same axis. We prove that null Cartan cubics lying on a timelike surface are normal isophotic curves with a spacelike axis and normal silhouette curves with a lightlike axis. We obtain the relation between Minkowski Pythagorean hodograph cubic curves and null Cartan normal isophotic and normal silhouette curves. Finally, we give numerical examples of null Cartan normal isophotic and normal silhouette curves obtained by integrating the system of two the first order differential equations under the initial conditions.
In this paper, the security of the optical image cryptosystem based on interference and an amplitude mask has been evaluated. The silhouette problem existing in the conventional interference-based ...cryptosystem is removed by eradicating the dependence of two phase-only masks (POM), which fixed the security leak. Moreover, the key space is enlarged by generating the amplitude mask in the encryption path as an additional private key. Since the number of unknown keys increases, the improved cryptosystem could be immune to the phase-retrieval technique-based iterative attack which the classical interference-based scheme is vulnerable to. However, the random phase mask (RPM) used as phase lock is predefined in the encryption path and irrelative to the plaintext. According to this finding, hybrid algorithms including a known-plaintext attack (KPA) and phase-retrieval technique-based iterative processes with different constraints have been proposed to crack the security-enhanced cryptosystem based on interference and an amplitude mask. Numerical simulations have been carried out to demonstrate that the silhouette problem existing in this enhanced scheme could be released by our proposed attacks.
•Evaluated the security strength of the interference-based cryptosystem with an amplitude mask.•Proposed hybrid attacks including a KPA and phase-retrieval techniques with different constraints.•Employed the KPA to retrieve the random phase mask irrelative to the plaintexts.•Disclosed the silhouette problem existing in the scheme based on interference and an amplitude mask.
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