A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions

Hamandi, Shaymaa M.; Rahma, Abdul Monem S.; Hassan, Rehab F.

doi:10.1109/acit50332.2020.9300069

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…They are typically not particularly robust because a modification to a portion of the image might lead to failure because it will alter the descriptor that is produced. An image patch is described by a local descriptor [37]. An image is matched using a variety of local descriptors, which makes the process more robust because not all of the descriptors must match in order for a comparison to be established.…”

Section: Hand-crafted Featuresmentioning

confidence: 99%

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

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Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn't expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes.

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Section: Hand-crafted Featuresmentioning

confidence: 99%

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

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“…A feed-forward neural network is an artificial neural network wherein connections between the nodes do not form a cycle [9], in this network as shown in fig. 6, the data moves in one direction, only beginning with the input nodes, through the hidden nodes ending with the output nodes.…”

Section: Feed Forward Neural Network Techniquementioning

confidence: 99%

Design A Multi Biometric System for Safe Access to Buildings

Rahma

Hassan²,

Hamandi³

2021

Al-Qadisiyah Journal of Pure Science

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Designing a multi-biometric system for safe access to buildings is a very critical process because this system will act as an alternative of humans in observation, and must make smart decisions to protect the building from any intruder depending on the biometric information of the entering persons. In this paper a full design of a proposed multi-biometric system will be presented, two sensors are used (Visible and Thermal). the proposed system consists of several subsystems The first is the construction of a database that contains all the photos and information about each person who belongs to the building, the central system using visible and thermal cameras, and the peripheral system using visible cameras only. The central system is capable of remotely identifying and ensure of the health condition (Temperature, Heart Rate, Respiration Rate) of each entering person which is a very important process, especially with the COVID-19 pandemic. The peripheral system monitors any suspicious issue like crowed or walking outside allowed lines.

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A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions

Cited by 2 publications

References 34 publications

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey

Design A Multi Biometric System for Safe Access to Buildings

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