Capsule Network-Based Deep Transfer Learning Model for Face Recognition

Sreekala, Keshetti; Cyril, C. Pretty Diana; Neelakandan, S.; Chandrasekaran, Saravanan; Walia, Ranjan; Martinson, Eric Ofori

doi:10.1155/2022/2086613

Cited by 24 publications

(6 citation statements)

References 34 publications

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“…An experiment was evaluated in which the collected player images were recognized and tested on state-of-the-art face recognition algorithms, including PCA [58], AdaBoost-LDA [43], CNN [59], and Capsule Network used for character recognition [60], in order to assess the robustness of the face recognition algorithm used in this model. The state of certain algorithms caused images of faces to be transformed to 128 × 128 pixels.…”

Section: Comparison With Cutting-edge Algorithmsmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Section: Comparison With Cutting-edge Algorithmsmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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“…11. Face recognition (FR) is a technique for recognizing individuals through face photographs [24]. 12.…”

Section: A Face Recognitionmentioning

confidence: 99%

Face Recognition for Automatic Border Control: A Systematic Literature Review

Hidayat,

Elviani,

Situmorang

et al. 2024

IEEE Access

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Context: Facial recognition is one aspect of research that still has broad potential for research and development, especially as a security system for automatic border control. There is a significant continuous need to understand the characteristics of system development by considering system complexity and implementation environmental conditions. Objective: This research aims to provide in-depth insight and assist researchers and practitioners in developing large-scale facial detection systems for automatic border control. It has a high level of complexity that necessitates special attention to several factors such as real-time system, privacy, variations in facial features, quantity of data, model, and implementation environment. Method: This study used a systematic literature review as a research methodology by Kitchenham. The analysis was based on studies published between 2019 and 2023 on using facial recognition in autonomous border control. A systematic analysis of research was conducted by examining 112 scientific studies from 7884 papers in scientific databases. Result: Based on research questions, 12 types of threats are often encountered in ABC face recognition, which can be seen in section IV. The method most widely used is deep learning, especially for detecting emotional features and morphing attacks. Apart from that, most datasets used are private because they require collaboration with organizations and are related to privacy. Three remaining issues are encountered in this research, including face recognition methodology, privacy, and architecture for large-scale development. Future directions: This study suggests two future research topics to enhance achieving desired results in large-scale and complex advancements in a methodical and structured while upholding privacy ethics.INDEX TERMS Automatic border control, big data, Face recognition, large-scale facial detection.

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“…This suggests that the two variables are entirely uncorrelated and independent of each other. The greater the difference between the observed and expected frequencies, the greater degree of correlation and the larger a chi square statistic that can be produced [19].…”

Section: Our Algorithmmentioning

confidence: 99%

Developing Business Management Concepts in the Economic Field Based on Artificial Intelligence in Light of Increasing Data

Rashid,

Ali,

Mousa

et al. 2024

Preprint

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Asia has formally entered the big data era thanks to the ongoing advancement of information technology. the company business management has discovered bright operations management based on technological advances, and the arrival of the big data era has improved work efficiency and brought about a number of economic benefits while also posing a number of challenges, such as the ongoing expansion of business scope. The business's current management model is unable to keep up with the demands of development as its scope continues to grow. The primary development trend for the future is the integration of intelligence technologies with enterprise business management, big data technology application in corporate business management promotes fairness, transparency, and justice across corporate development and consumer activities; it also facilitates the production of goods and the creation of value for products in the direction of socialization. In order to further deepen the understanding of artificial intelligence among relevant personnel, we aim to further elaborate the future growth direction of the application of artificial intelligence in the field of enterprise business management in this paper, which begins with an in-depth awareness of the current situation of the combined use of artificial intelligence technology and big companies’ business management. We then focus on the utilization path of artificial cognitive ability technology in businesses business management.

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Capsule Network-Based Deep Transfer Learning Model for Face Recognition

Cited by 24 publications

References 34 publications

Automatic Player Face Detection and Recognition for Players in Cricket Games

Automatic Player Face Detection and Recognition for Players in Cricket Games

Face Recognition for Automatic Border Control: A Systematic Literature Review

Developing Business Management Concepts in the Economic Field Based on Artificial Intelligence in Light of Increasing Data

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