Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh, Jamil Abedalrahim Jamil; -, Irianto; Aziz, Azwan; Xin, Chang Kai; Hossain, A. K. M. Zakir; Herawan, Safarudin Gazali

doi:10.14569/ijacsa.2022.0130663

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Because of this, an ensemble model is proposed, based on the simultaneous division of the search space into subgroups and the evolution of models for such subgroups. As a result, the models were able to identify more specific dependencies and relationships between signals [20][21][22].…”

Section: Proposed Methodsmentioning

confidence: 99%

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Leoshchenko,

Oliinyk,

Subbotin

et al. 2023

Computer Modeling and Intelligent Systems

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This paper is devoted to the problem of detecting and classifying anomalies for time series data. Some of the important applications of time series anomaly detection are healthcare, fraud detection, and system failure recognition. Despite scientists extensive experience in detecting anomalies in time series, most methods look for individual objects that differ from ordinary objects, but do not take into account the specifics of the data sequence [1]. In this paper, a method for detecting anomalies and clastering time series based on neuroevolutionary approaches is proposed. Prediction-based methods are used to detect anomalies: statistical and deep neural networks are used. Classical clustering methods that accept statistical parameters of series were used for clustering.

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Section: Proposed Methodsmentioning

confidence: 99%

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Leoshchenko,

Oliinyk,

Subbotin

et al. 2023

Computer Modeling and Intelligent Systems

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“…2. To enable the gadget to connect to Wi-Fi or a mobile hotspot, the author [12] sets the email account that will receive the recorded photo in addition to the device's SSID and password. Deep sleep mode will be the device's first state.…”

Section: Figure 1 Flowchart Of Custom Face Datasetmentioning

confidence: 99%

Deep Learning-Based Small Face Detection from Hard Image

Sapna Shinde,

Priti Chakurkar,

Rashmi Rane

2024

Int Res J Adv Engg Hub

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Facial detection usually comes first in face recognition and face analysis systems. Previously, techniques such as directed gradient histograms and cascades relied on manually-engineered features from particular photos. Nevertheless, the precision with which these techniques could identify faces in uncontrolled environments was restricted. Numerous deep learning-based face recognition frameworks have recently been developed, many of which have significantly increased accuracy, as a result of the rapid progress of deep learning in computer vision. Despite these advancements, detecting small, scaled, positioned, occluded, blurred, and faces that are partially occluded in uncontrolled conditions remains a challenge in face identification. This problem has been studied for many years but has not been completely resolved.

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Artificial Intelligence Techniques Application in the Mobile Device Data Analysis to Identify Person-of-Interest

Fedynyshyn,

Mykhaylova

2024

CSN

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The methods for identifying persons of interest (POI) based on mobile device data has been considered. The problem is relevant and unresolved in the activities of law enforcement intelligence and other agencies involved in operational search activities due to the large amount of data stored on mobile devices. Given the complexity and volume of mobile data traditional analysis methods are often insufficiently effective. The authors propose use of artificial intelligence (AI) including machine learning and natural language processing to improve the efficiency and speed of mobile device data analysis. This approach aims to overcome the limitations of manual data analysis and enhance the process of identifying POIs while adhering to the principles of forensic integrity. The research specifically demonstrates how machine learning can be utilized to search for persons of interest in WhatsApp messenger data. A method has been developed for decentralized control of adaptive data collection processes using the principle of equilibrium and reinforcement learning using the normalized exponential function method. The developed method allows for efficient operation of autonomous distributed systems in conditions of dynamic changes in the number of data collection processes and limited information interaction between them. The results of the experiment indicate that using artificial intelligence for facial recognition may result in false positive outcomes implying that humans cannot be entirely replaced at the current stage of AI evolution. However the application of deep learning showed an 88% success rate in facial recognition. These findings underscore the transformative potential of artificial intelligence in mobile forensics highlighting its capacity to enhance the accuracy and efficiency of data analysis in mobile devices. Key words: artificial intelligence mobile forensics data analysis ios whatsapp

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Face Recognition System Design and Implementation using Neural Networks

Cited by 3 publications

References 25 publications

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Deep Learning-Based Small Face Detection from Hard Image

Artificial Intelligence Techniques Application in the Mobile Device Data Analysis to Identify Person-of-Interest

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