Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies

Manssor, Samah A. F.; Sun, Shanhui; Elhassan, Mohammed A. M.

doi:10.3390/s21134323

Cited by 28 publications

(12 citation statements)

References 62 publications

(87 reference statements)

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“…By analyzing motion related diseases and their association with gait, prediction and later treatment assistance can be made. Gait recognition technology based on computer vision can make gait analysis faster, more accurate, and more objective (Manssor et al 2021). By modeling and analyzing video image data during exercise, the gait recognition system can extract features such as athlete's posture, contour, speed, and period, achieving precise recognition of gait (Verlekar et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Gait analysis of athletes based on fiber optic sensors and computer vision algorithms

Liu

2024

Preprint

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Walking may seem simple, but it actually involves complex control processes. Walking is accomplished through a series of collaborative operations, including coordinated control, balance control, central command, and various other physiological mechanisms. When problems arise between these links, it may cause abnormal gait or motor injury. Gait analysis of athletes can help coaches and medical personnel evaluate their athletic skills and physical health. Therefore, this article aims to develop an effective athlete gait analysis method based on fiber optic sensors and computer vision algorithms. Fiber optic sensors capture subtle changes in athletes' gait by measuring the changes in optical signals in the fiber optic. The collected gait data includes parameters such as stride length, stride frequency, and gait phase. Step length refers to the distance traveled during a walk, providing detailed information about an athlete's gait and helping to evaluate their athletic skills and physical health. Using computer vision algorithms to process and analyze the collected gait data, accurate gait parameters are obtained for identifying athletes' walking patterns and identifying abnormal gait.

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Section: Introductionmentioning

confidence: 99%

Gait analysis of athletes based on fiber optic sensors and computer vision algorithms

Liu

2024

Preprint

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“…These are based on a structure inspired by the human brain for analyzing com-plex and unstructured information [7]. This has facilitated the processing of large amounts of information from images, text, and sound, the analysis of which is not feasible with traditional methods due to ambiguities in the information [8]. Based on this, intelligent systems have been developed to provide fall risk information, classified according to the type of sensor used and the function of the system [9].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision System Based on the Analysis of Gait Features for Fall Risk Assessment in Elderly People

Cedeno-Moreno,

Malagon-Barillas,

Morales-Hernandez

et al. 2024

Applied Sciences

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Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of establishing a preventive system for the care of the elderly, both in the hospital environment and at home. Therefore, this work proposes the development of an intelligent vision system that uses a novel methodology to infer fall risk from the analysis of kinetic and spatiotemporal gait parameters. In general, each patient is assessed using the Tinetti scale. Then, the computer vision system estimates the biomechanics of walking and obtains gait features, such as stride length, cadence, period, and range of motion. Subsequently, this information serves as input to an artificial neural network that diagnoses the risk of falling. Ninety-six participants took part in the study. The system’s performance was 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score. Thus, the proposed system can evaluate the fall risk assessment, which could benefit clinics, hospitals, and even homes by allowing them to assess in real time whether a person is at high risk of falling to provide timely assistance.

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“…With the continuous development of social informatization, information security has gradually been widely considered by national security, public security, banking systems, e-commerce, and other fields with a high demand for information security. In recent years, the field of identity recognition technology has gradually become a popular subject at home and abroad [ 1 , 2 ]. Traditional identification technology is mainly based on personal documents or passwords, which cause many inconveniences to the public because it is inconvenient to carry or forget them easily.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Xue

Wang

2023

Computational Intelligence and Neuroscience

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In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

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Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies

Cited by 28 publications

References 62 publications

Gait analysis of athletes based on fiber optic sensors and computer vision algorithms

Gait analysis of athletes based on fiber optic sensors and computer vision algorithms

Computer Vision System Based on the Analysis of Gait Features for Fall Risk Assessment in Elderly People

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

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